# data[].attributes.headline
Cellphone tracking could help stem the spread of coronavirus. Is privacy the price?

# data[].attributes.subject
Cellphone tracking could help stem the spread of coronavirus. Is privacy the price?

# data[].attributes.body
This article talks about the use of an app/ tracking software similar to what is already in place in South Korea and China to be implemented in the U.S. as well to be able to help slow the spread of the Corona virus.

-Alex Campo

# data[].attributes.attachment_link.readable_text
"IT IS POSSIBLE TO STOP THE EPIDEMIC." That's the message splashed atop a website built by a University of Oxford team this week to share new research on the spread of the novel coronavirus. Below that hopeful statement comes a big caveat: To stop the virus' spread, health officials need to find and isolate the contacts of infected people-lots of them-and fast. Such contact tracing is a mainstay of infectious disease control. But the Oxford team is one of several now advocating for a new approach: tapping into cellphone location data to track the spread of infection and warn people who may have been exposed.

Several governments in Asia have tried that approach in ways that would run afoul of privacy laws in many other countries. China, for example, has reportedly relied on mass surveillance of phones to classify individuals by their health status and restrict their movements. Now, research teams in Europe and the United States are considering less invasive ways to collect and share data about infections, and some are already developing and testing coronavirus-specific phone apps. Governments, meanwhile, are scrambling to figure out how these potential pandemic-fighting tools could work within data privacy laws and without losing the support of an already wary public.

"We don't live in a culture of public trust when it comes to data," says David Leslie, an ethicist at the Alan Turing Institute who studies the governance of data-driven technologies. "We live in this age that has been called the age of surveillance capitalism, where ... our data is abused and exploited." But, he adds, authorities and the public will have to weigh the value of privacy against the possibility that data collection could save millions of lives. "These are not normal times."

Behind in the chase

Tracing the people an infected person recently came near requires widespread testing to diagnose infections in the first place. That testing has been painfully slow to ramp up in the United States and parts of Europe-to say nothing of countries elsewhere with fewer resources. Even as more tests become available, state and local health departments may not be able to interview every patient and follow up with every contact. And even the most painstaking interview can't reveal contacts or places that a person just doesn't remember.

The virus that is causing the pandemic, severe acute respiratory syndrome coronavirus 2, poses a particular challenge, says Oxford infectious disease epidemiologist Christophe Fraser. So far, it appears that nearly half of transmissions happen before an infected person has symptoms. That means the virus can spread for days before health authorities even learn of a spreader. "No matter how many resources you put into [contact tracing], it's never going to keep ahead of the virus," he says. "It's always going to be one or two generations ahead."

Fraser and his colleagues have watched U.K. coronavirus cases climb in recent weeks while new cases in China plummeted. Two postdoctoral researchers in his lab described their families in China using a phone app that told them when and where it was safe to go out. "We thought, 'Well, what would we do, if we [built] an app?'"

Fraser, with Oxford clinician David Bonsall and colleagues, designed a mathematical simulation of how "instantaneous digital contact tracing" would influence the spread of the virus. To stop the epidemic, health officials must reduce the virus' reproductive number-the average number of people each infected person transmits the virus to-to less than one. When the team modeled a scenario in which contacts were notified the instant a person tested positive, it was possible to push the reproductive rate of the virus below that threshold, the team reported in a preprint this week.

"It became obvious to us that this was solving a major problem," says Fraser, who adds that his team is advising several European governments, including the United Kingdom's, on digital tracking. The U.K. National Health Service announced on 19 March that it was developing a coronavirus contact tracing app.

Digital tracking of many flavors

At its simplest, digital contact tracing might work like this: Phones log their own locations; when the owner of a phone tests positive for COVID-19, a record of their recent movements is shared with health officials; owners of any other phones that recently came close to that phone get notified of their risk of infection and are advised to self-isolate. But designers of a tracking system will have to work out key details: how to determine the proximity among phones and the health status of users, where that information gets stored, who sees it, and in what format.

Digital contact tracing systems are already running in several countries, but details are scarce and privacy concerns abound. Protests greeted Israeli Prime Minister Benjamin Netanyahu's rollout this week of a surveillance program that uses the country's domestic security agency to track the locations of people potentially infected with the virus. South Korea has released detailed information on infected individuals-including their recent movements-viewable through multiple private apps that send alerts to users in their vicinity. "They're essentially texting people, saying, 'Hey, there's been a 60-year-old woman who's positive for COVID. Click this for more information about her path,'" says Anne Liu, a global health expert at Columbia University. She warns that the South Korean approach risks unmasking and stigmatizing infected people and the businesses they frequent.

But digital tracking is probably "identifying more contacts than you would with traditional methods," Liu says. A contact-tracing app might not have much impact in a city where a high volume of coronavirus cases and extensive community transmission has already shuttered businesses and forced citizens inside, she adds. But it could be powerful in areas, such as in sub-Saharan Africa, that are at an earlier stage of the outbreak, and where isolating potential cases could avert the need to shut down all schools and businesses. "If you can package this type of information in a way that protects individual privacy as best you can, it can be something positive," she says.

Navigating privacy laws

In countries with strict data privacy laws, one option for collecting data is to ask telecommunications and other tech companies to share anonymous, aggregated information they've already gathered. Laws in the United States and the European Union are very specific about how app and device users must consent to the use of their data-and how much information companies must disclose about how those data will be used, stored, and shared. Working within those constraints, mobile carriers in Germany and Italy have started to share cellphone location data with health officials in an aggregated, anonymized format. Even though individual users aren't identified, the data could reveal general trends about where and when people are congregating and risk spreading infection.

Google and Facebook are both in discussions with the U.S. government about sharing anonymized location data, The Washington Post reported this week. U.S. companies have to deal with a patchwork of state and federal privacy regulations, says Melissa Krasnow, a privacy and data security partner at VLP Law Group. App and devicemakers could face user lawsuits for sharing data in a way that wasn't originally specified in their terms of service-unless federal or local officials pass legislation that would free them from liability. "Now you've got a global pandemic, so you would think that [you] would be able to use this information for the global good, but you can't," Krasnow says. "There's expectations about privacy."

Another option is to start fresh with a coronavirus-specific app that asks users to voluntarily share their location and health data. For example, a basic symptom-checking app could do more than just keeping people who don't need urgent care out of overstretched emergency rooms, says Samuel Scarpino, an epidemiologist at Northeastern University. Health researchers could use also use location data from the app to estimate the size of an outbreak. "That could be done, I think, without risking being evil," he says.

For Scarpino, the calculus changes if governments want to track the movements of a specific person who has coronavirus relative to the paths of other people, as China and South Korea have apparently done. That kind of tracking "could easily swing towards a privacy violation that isn't justified by the potential public health benefit," he says.

In Germany, which has some of Europe's strictest data privacy protections, the government can compel a technology company to share location data on an individual in the interest of national security, says Sebastian Golla, a legal scholar at the Johannes Gutenberg University of Mainz who studies data protection law. But indiscriminate mass tracking of individuals lacks a legal basis, he says. To track people who have or might have coronavirus, Germany and other European countries would need to pass laws specifying how data collection would be restricted to a certain population, for a certain time, and for a certain purpose.

Such laws could be on the way. On 21 March, Frankfurter Allgemeine Zeitung reported that the German health ministry had drafted changes to a law called the Infection Protection Act to allow, among other things, the tracking of people who were in contact with those infected with the coronavirus.

The next generation of coronavirus trackers

Several emerging projects aim to set up voluntary, privacy-conscious phone tracking systems. This week, a team led by computer scientist Ramesh Raskar at the Massachusetts Institute of Technology released a prototype of an app called Private Kit: Safe Paths. The app stores up to 28 days of a user's GPS location data, logged every 5 minutes. If the user tests positive for coronavirus, they can choose to share their recent data with health officials to identify and publicize the places where others may have been at risk of infection.

A future iteration of the app, soon to be released, would compare a user's recent locations against the path of an infected person and alert them of potential contact. Users wouldn't learn anything else about the infected person-not their age, their sex, or their geographic path. The team, which includes collaborators from Harvard University and the Mayo Clinic, is in discussions with "a dozen cities and nations in all parts of the world," about running pilot trials of the app, Raskar says.

Another app in development in Germany relies partly on location data that Google already stores for its account holders. A person who tests positive could use the app-called GeoHealth-to "donate" their location history. That data would then be anonymized and stored on a central server, says Gernot Beutel, a stem cell transplant physician at Hannover Medical School who is co-developing the technology. A data analytics platform designed by the software company Ubilabs would compare users' movement history to that of infected people, and the app would show them color-coded alerts based on how recently they may have encountered the virus. Though a combination of GPS tracking, wireless network data, and connections between phones via Bluetooth, Beutel says the app should be able to detect when a phone comes within 1 meter of another phone.

Making data submissions voluntary and anonymizing data are "good options to maintain civil rights. It's a clean way of legally doing it," Golla says. But such apps will reduce the spread of disease only if a lot of people use them. Liu cautions that because a tracking app can't capture every possible source of infection, it risks creating a false sense of security for users. "Just because you don't see a dot on a map where a contact might have been doesn't mean that areas that don't have dots don't have infected people."

Raskar's team is working to figure out how many people would need to use the Private Kit app for it to be effective at controlling disease spread. He can't say how many people have downloaded the prototype; the researchers don't collect that information for privacy reasons, he says. Beutel is hopeful that the urgency of the pandemic will inspire much of the population to lay bare their movements and health status. "People give their stem cells for patients that need a stem cell transplantation. They give their blood," he says. "We hope that people think about the crisis, and are willing to give their data."
With reporting by Catherine Matacic.

doi:10.1126/science.abb8296

Kelly is a staff writer at Science.

More from News

# data[].attributes.attachment_link.title
Cellphone tracking could help stem the spread of coronavirus. Is privacy the price?

# data[].attributes.attachment_link.description
"IT IS POSSIBLE TO STOP THE EPIDEMIC." That's the message splashed atop a website built by a University of Oxford team this week to share new research on the spread of the novel coronavirus. Below that hopeful statement comes a big caveat: To stop the virus' spread, health officials need to find and isolate the contacts of infected people-lots of them-and fast.

# data[].attributes.body
https://youtu.be/Peodc_ZRT2k

https://www.wired.com/story/zoom-bug-webcam-hackers/

https://vm.tiktok.com/nFT7n9/

-Juliana C Medel

# data[].attributes.body
Basel Action Network is one of the only organizations that is trying to get companies to recycle properly.
https://www.youtube.com/watch?v=FmJFVmtWf-I

Where you can find proper e waste recycling: http://e-stewards.org/find-a-recycler/

# data[].attributes.body
Post your comments about Local Contexts (and TK labels) below. Make sure to include your name.

# data[].attributes.body
Post your comments about PPWP below. Make sure to include your name.

# data[].attributes.attachment_link.title
Plateau Peoples’ Web Portal | Plateau Peoples’ Web Portal

# data[].attributes.attachment_link.description
k’úusey’ne sapoˀsaapóˀs (Woman’s Saddle) Nez Perce
In 1836, Henry Spalding and his wife Eliza joined Marcus and Narcissa Whitman on a mission to the Oregon Country. In 1846, Spalding acquired Nez Perce clothing, artifacts, and horse gear which he shipped to his friend and supporter, Dr. Dudley Allen…

# data[].attributes.body
Post your comments about Mukurtu below.Make sure to include your name.

# data[].attributes.headline
Phones Could Track the Spread of Coronavirus. Is it a good idea? 

# data[].attributes.subject
Phones Could Track the Spread of Coronavirus. Is it a good idea? 

# data[].attributes.attachment_link.readable_text
In 2011, two scientists at Cambridge University in the UK devised a clever way to measure and model the spread of the flu-an app called FluPhone that used Bluetooth and other wireless signals as a proxy for interactions between people, and asked users to report flu-like symptoms.

If you'd had lunch with someone who later got sick, FluPhone would let you know. Besides slowing the spread of the flu, the app promised to help health authorities monitor and model the spread of influenza. FluApp made headlines and the front page of the BBC website at the time. But in the end fewer than 1 percent of people in Cambridge signed up to use it.

As the deadly Covid-19 respiratory virus stalks the US, some techies suggest using smartphones to track and report transmissions. The idea raises many questions, including how well such a system would actually work, whether it might sow unnecessary alarm or confusion, and whether such tools might enable unwanted corporate or government surveillance.

The creators of FluPhone, Jon Crowcroft and Eiko Yoneki, certainly believe an app like theirs could help fight the coronavirus.

"The health protection agencies could use it to populate anonymized map data," which might help reduce transmission, Crowcroft says. He says an app would also help researchers learn "how long the virus survives on a surface, what fraction of the population are asymptomatic carriers, and where to target critical medical resources."

Inspired by the way China and South Korea apparently used smartphones to slow the spread of Covid-19, some US technologists have begun working on tracking apps. An open source project called CoEpi sprang up in February to develop an app with similar functionality to FluPhone. Ramesh Raskar, a professor at the MIT Media Lab, and colleagues are developing an app that would let people log their movements, and compare them with those of known coronavirus patients, using redacted data supplied by the state or national public health departments. Over time, users would be asked whether they are infected, providing a way to identify potential transmissions in a similar way to FluPhone. The team released a prototype for testing on Friday.

Raskar has been rallying other researchers and tech executives to the effort, and he has been in contact with the World Health Organization, the US Centers for Disease Control and Prevention, and the US Department of Health and Human Services. "They are giving us guidance on what will work," he says, although none has yet endorsed the idea.

Stefan Germann, CEO of the Botnar Foundation, a Swiss organization focused on health and child welfare, has been advising Raskar. He says the proposed app "has strong potential" but should be tested in a single "sandbox" city first. "It is important to respond fast, but not to rush efforts," he says.

Others suggest similar technology be added to smartphones by default. An open letter signed by several dozen prominent technologists, executives, and clinicians, posted on Tuesday, called on the tech industry to do more to combat the coronavirus. Among other things, the group recommended that Apple and Google update their smartphone software to make it possible to track contact between people, providing users grant permission. Apple and Google did not return requests for comment.

"If such a feature could be built before SARS-CoV-2 is ubiquitous, it could prevent many people from being exposed," the letter suggests. "In the longer term, such infrastructure could allow future disease epidemics to be more reliably contained, and make large scale contact tracing of the sort that has worked in China and [South] Korea, feasible everywhere."

Peter Eckersley, a distinguished technology fellow at the Electronic Frontier Foundation and a signer of the letter, says it should be possible to implement such a system without establishing a national database that could enable government surveillance. "The checks could happen privately on your own phone" or with advanced security software, he says.

# data[].attributes.attachment_link.title
Phones Could Track the Spread of Covid-19. Is It a Good Idea?

# data[].attributes.attachment_link.description
In 2011, two scientists at Cambridge University in the UK devised a clever way to measure and model the spread of the flu-an app called FluPhone that used Bluetooth and other wireless signals as a proxy for interactions between people, and asked users to report flu-like symptoms. If you'd had lunch with someone who later got sick, FluPhone would let you know.

# data[].attributes.headline
"Gamers" Called to Help Scientist Fight COVID-19

# data[].attributes.subject
"Gamers" Called to Help Scientist Fight COVID-19

# data[].attributes.body
Online game that helps solve puzzles and recognize spatial relationships in diseases.

# data[].attributes.body
We'll get to talking about student data collection later this semester, but recall the "panopticon" type of prison and surveillance theory? "Panopto" is an interesting name for recording software

(The man who is pictured is Michel Foucault, the theorist who coined "panopticism")

# data[].attributes.headline
Clearview AI app banned from Apples App Store.

# data[].attributes.subject
Clearview AI app banned from Apples App Store.

# data[].attributes.attachment_link.readable_text
An iPhone app built by controversial facial recognition startup Clearview AI has been blocked by Apple, effectively banning the app from use.

Apple confirmed to TechCrunch that the startup "violated" the terms of its enterprise developer program.

The app allows its users - which the company claims it serves only law enforcement officers - to use their phone camera or upload a photo to search its database of 3 billion photos. But BuzzFeed News revealed that the company - which claims to only cater to law enforcement users - also includes many private-sector users, including Macy's, Walmart and Wells Fargo.

Clearview AI has been at the middle of a media - and legal - storm since its public debut in The New York Times last month. The company scrapes public photos from social media sites, drawing ire from the big tech giants that claim Clearview AI misused their services. But it's also gained attention from hackers. On Wednesday, Clearview AI confirmed a data breach in which its client list was stolen.

TechCrunch found Clearview AI's iPhone app on an public Amazon S3 storage bucket on Thursday, despite a warning on the page that the app is "not to be shared with the public."

The page asks users to "open this page on your iPhone" to install and approve the company's enterprise certificate, allowing the app to run.

But this, according to Apple's policies, is prohibited if the app's users are outside of Clearview AI's organization.

Enterprise certificates are issued by Apple to allow companies to build and approve iPhone and iPad apps designed for internal company use only. It's common for these certificates to be used to test apps internally before they are pushed out to the App Store. Apple maintains a strict set of rules on use of enterprise certificates, and says they cannot be used by consumers. But there have been cases of abuse.

Last year, TechCrunch exclusively reported that both Facebook and Google were using their enterprise certificates for consumer-facing apps in an effort to bypass Apple's App Store. Apple revoked the tech giants' enterprise certificates, disabling the infracting app but also any other app that relied on the certificate, including their catering and lunch menu apps.

The app was labeled as "beta" - typically a pre-release or a test version of the app. Besides this claim, there is no evidence to suggest this app was not used by Clearview AI customers.

Clearview AI chief executive Hoan Ton-That told TechCrunch: "We are in contact with Apple and working on complying with their terms and conditions."

A brief analysis of the app through network traffic tools and disassembly tools shows it works largely in the same way as Clearview AI's Android app, which was discovered by Gizmodo on Thursday.

Like the Android app, a user needs a Clearview AI-approved username and password to use the app.

# data[].attributes.attachment_link.title
Apple has blocked Clearview AI's iPhone app for violating its rules

# data[].attributes.attachment_link.description
An iPhone app built by controversial facial recognition startup Clearview AI has been blocked by Apple, effectively banning the app from use. Apple confirmed to TechCrunch that the startup "violated" the terms of its enterprise developer program.

# data[].attributes.body
Clearview AI's client list has been breached/leaked. Though the company originally said they were only working with law enforcement, the list also includes companies like Macy's, Walmart, the NBA, as well as some universities.

"the numbers are staggering and illustrate how Clearview AI, a small startup founded three years ago, has been able to get its software to employees at some of the world’s most powerful organizations. According to documents reviewed by BuzzFeed News, people associated with 2,228 law enforcement agencies, companies, and institutions have created accounts and collectively performed nearly 500,000 searches — all of them tracked and logged by the company."

# data[].attributes.attachment_link.readable_text
The United States' main immigration enforcement agency, the Department of Justice, retailers including Best Buy and Macy's, and a sovereign wealth fund in the United Arab Emirates are among the thousands of government entities and private businesses around the world listed as clients of the controversial facial recognition startup with a database of billions of photos scraped from social media and the web.

The startup, Clearview AI, is facing legal threats from Facebook, Google, and Twitter, as well as calls for regulation and scrutiny in the US. But new documents reviewed by BuzzFeed News reveal that it has already shared or sold its technology to thousands of organizations around the world.

In its quest to create a global biometric identification system to span both public and private sectors, Clearview has signed paid contracts with US Immigration and Customs Enforcement (ICE), the US Attorney's Office for the Southern District of New York, and Macy's, according to the document obtained by BuzzFeed News. The company has credentialed users at the FBI, Customs and Border Protection (CBP), Interpol, and hundreds of local police departments. In doing so, Clearview has taken a flood-the-zone approach to seeking out new clients, providing access not just to organizations, but to individuals within those organizations - sometimes with little or no oversight or awareness from their own management.

Clearview's software, which claims to match photos of persons of interest to online images culled from millions of sites, has been used by people in more than 2,200 law enforcement departments, government agencies, and companies across 27 countries, according to the documents. This data provides the most complete picture to date of who has used the controversial technology and reveals what some observers have previously feared: Clearview AI's facial recognition has been deployed at every level of American society and is making its way around the world.

The New York-based startup has claimed its controversial technology is intended as a tool for police and that it was prioritizing business in North America. "It's strictly for law enforcement," Clearview CEO Hoan Ton-That said on Fox Business earlier this month. He noted in a Feb. 5 statement to BuzzFeed News that his company was "focused on doing business in USA and Canada." But in reality, Clearview AI has also been aggressively pursuing clients in industries such as law, retail, banking, and gaming and pushing into international markets in Europe, South America, Asia Pacific, and the Middle East.

In reply to an extensive list of questions, Clearview attorney Tor Ekeland said, "There are numerous inaccuracies in this illegally obtained information. As there is an ongoing Federal investigation, we have no further comment."

Clearview has attracted a whirlwind of attention for claiming it had built unprecedented facial recognition trained on an ever-increasing database of more than 3 billion photos ripped from Facebook, Instagram, YouTube, and other websites. In a January interview with the New York Times, Ton-That said the company was working with 600 law enforcement agencies across the country and had provided the software, which can be used on a desktop computer or through a mobile app, to the FBI and Department of Homeland Security.

AMR ALFIKY / The New York Times / Redux

Clearview AI CEO Hoan Ton-That in New York, Jan. 10, 2019.

The internal documents, which were uncovered by a source who declined to be named for fear of retribution from the company or the government agencies named in them, detail just how far Clearview has been able to distribute its technology, providing it to people everywhere, from college security departments to attorneys general offices, and in countries from Australia to Saudi Arabia. BuzzFeed News authenticated the logs, which list about 2,900 institutions and include details such as the number of log-ins, the number of searches, and the date of the last search. Some organizations did not have log-ins or did not run searches, according to the documents, and BuzzFeed News is only disclosing the entities that have established at least one account and performed at least one search.

Even with that criteria, the numbers are staggering and illustrate how Clearview AI, a small startup founded three years ago, has been able to get its software to employees at some of the world's most powerful organizations. According to documents reviewed by BuzzFeed News, people associated with 2,228 law enforcement agencies, companies, and institutions have created accounts and collectively performed nearly 500,000 searches - all of them tracked and logged by the company.

While some of these entities have formal contracts with Clearview, many do not. A majority of Clearview's clients are using the tool via free trials, most of which last 30 days. In some cases, when BuzzFeed News reached out to organizations from the documents, officials at a number of those places initially had no idea their employees were using the software or denied ever trying the facial recognition tool. Some of those people later admitted that Clearview accounts did exist within their organizations after follow-up questions from BuzzFeed News led them to query their workers.

"This is completely crazy," Clare Garvie, a senior associate at the Center on Privacy and Technology at Georgetown Law School, told BuzzFeed News. "Here's why it's concerning to me: There is no clear line between who is permitted access to this incredibly powerful and incredibly risky tool and who doesn't have access. There is not a clear line between law enforcement and non-law enforcement."

"This is completely crazy. ... There is not a clear line between law enforcement and non-law enforcement."

There are currently no federal laws regulating the use of facial recognition, though several elected officials have proposed bills. States including Illinois have developed regulations on the corporate use of biometric data, and some cities have outright banned the technology. In that regulatory vacuum, Clearview has thrived, doling out free trials seemingly at will and encouraging law enforcement officers and officials to invite their colleagues and perform as many searches as possible.

On Wednesday, Clearview AI told the Daily Beast that an intruder had "gained unauthorized access to its list" of customers. "Unfortunately, data breaches are part of life in the 21st century. Our servers were never accessed," Ekeland told the Daily Beast. "We patched the flaw, and continue to work to strengthen our security."

The explanation did not sit well with some lawmakers, including Oregon Sen. Ron Wyden.

"Shrugging and saying data breaches happen is cold comfort for Americans who could have their information spilled out to hackers without their consent or knowledge," he told BuzzFeed News. "Companies that scoop up and market vast troves of information, including facial recognition products, should be held accountable if they don't keep that information safe."

Clearview CEO Ton-That has been coy about his company's relationship with the federal government, but documents reviewed by BuzzFeed News suggest his startup has deeply penetrated multiple departments and agencies there. Among them is the Department of Homeland Security, where employees at CBP, the country's main border security organization, are listed in the documents as having registered nearly 280 accounts. In total, those accounts have run almost 7,500 searches, the most of any federal agency that did not have some type of paid relationship.

A spokesperson for CBP said Clearview was not used for the agency's biometric entry-exit programs and declined further comment.

Agents at ICE have also used Clearview, according to company documents, running more than 8,000 searches from about 60 different accounts associated with a Homeland Security Investigations field office in El Paso, Texas, an ICE office in Cherry Hill, New Jersey, and a Border Enforcement Security Task Force at New York's John F. Kennedy Airport. The documents also indicate employees of ICE's Enforcement and Removal Operations, the body responsible for the arrest and deportation of those in the country without authorization, have tried Clearview.

A spokesperson for ICE told BuzzFeed News that HSI began a paid pilot program in June 2019 through its Child Exploitation Investigations Unit and noted that a formal contract has not yet been signed.

"ICE's use of facial recognition technology is primarily used by Homeland Security Investigations (HSI) special agents investigating child exploitation and other cybercrime cases," the spokesperson said. "ICE Enforcement and Removal Operations (ERO) officers have also occasionally used the technology, as task force officers with HSI and the Department of Justice, and through training, on human trafficking investigations."

Jacinta González, a senior campaign director at Mijente, a Latinx advocacy group, told BuzzFeed News that ICE's use of Clearview in the absence of a regulatory framework is troubling. "This tool goes way beyond anything that is legal, and there is literally no accountability for how they're going to use this tool," she said. "They could walk into a supermarket, scan people, see if it matches up, and deport them immediately."

The documents also show that employees at 10 fusion centers, intelligence intake facilities that are recognized by DHS, are deploying Clearview across the country and in the US Virgin Islands. One of those fusion centers in Louisiana was listed as a paying customer.

"They could walk into a supermarket, scan people, see if it matches up, and deport them immediately."

Clearview has also been used inside the Department of Justice, where the list of government organizations trialing the company's facial recognition software includes multiple offices at the US Secret Service (some 5,600 searches); the Drug Enforcement Administration (about 2,000 searches); the Bureau of Alcohol, Tobacco, Firearms, and Explosives (more than 2,100 searches); and the FBI (5,700 searches across at least 20 different field offices). Spokespeople for all these agencies either declined comment or did not respond to a request for comment.

Two DOJ organizations - the criminal intelligence branch of the US Marshals and the US Attorney's Office in the Southern District of New York - are paying to use Clearview. A spokesperson for the US Marshals said the organization "cannot confirm the use of any specific, sensitive equipment and techniques that may be deployed by law enforcement," while the US Attorney's Office did not respond to multiple requests for comment.

"Government agents should not be running our faces against a shadily assembled database of billions of our photos in secret and with no safeguards against abuse," Nathan Freed Wessler, a staff attorney with the ACLU, said to BuzzFeed News. "More fundamentally, that so many law and immigration enforcement agencies were hoodwinked into using this error-prone and privacy-invading technology peddled by a company that can't even keep its client list secure further demonstrates why lawmakers must halt use of face recognition technology, as communities nationwide are demanding."

Clearview's technology may have even made it to the White House. Documents reviewed by BuzzFeed News include an entry for "White House Tech Office" with a single user, who logged in back in September 2019 to perform six searches.

The White House did not confirm or deny if that was the case. "If a current or former staff member attempted to access more information about this product, it was not an official inquiry and was not sanctioned by the White House," a senior White House official told BuzzFeed News.

Beyond the federal government, Clearview AI's free trials have inspired facial recognition usage in hundreds of regional, state, county, and local law enforcement agencies. The Miami Police Department, for example, has run over 3,000 Clearview searches, according to the documents. The San Mateo County Sheriff's Office has run about 2,000 searches, as has the Philadelphia Police Department. The Indiana State Police, identified in the startup's documents as a paying agency, has run more than 5,700 scans.

The New York State Police, which has several users who have run dozens of searches, said Clearview is one of many tools used by the agency. The agency paid $15,000 for Clearview licenses, according to federal spending database GovSpend.

"The Clearview AI facial recognition software is used to generate potential leads in criminal investigations as well as homeland security cases involving a clearly identified public safety issue," a New York State Police representative said to BuzzFeed News.

The bulk of Clearview's paying customers are local and state police departments. The Atlanta Police Department, paid $6,000 for three licenses last year, according to documents obtained by BuzzFeed News. Officers in Wyomissing, Pennsylvania, paid $1,000 for a license, according to federal spending database GovSpend.

Clearview AI can be a powerful tool for local police. A representative for the Chicago Police Department - which paid $49,875 for two-year Clearview log-ins for 30 people - told BuzzFeed News that it is one of two types of facial recognition software the department uses. The first, DataWorks, uses an internal library of mugshots taken in and around the Chicago area. Clearview, meanwhile, employs more than 3 billion pictures from social media and "millions of websites," according to its CEO, creating a dragnet that could encompass the world. Users with Chicago police, whose contract with Clearview runs through 2021, have collectively run over 1,500 searches.

"If there's no match [on DataWorks], we try Clearview," a Chicago police representative said. "DataWorks is a closed system, so it only looks at photos we have. But Clearview uses open source media."

Jason Ercole, a captain with the Senoia Police Department, which is about 40 miles south of Atlanta, said he started with a free trial of Clearview before converting to a paid license and has since made one positive identification of a suspect who was allegedly cashing fake checks. He said he did not have to go through any training to obtain or use the software and noted he never uses a Clearview match as the sole basis for obtaining a warrant for arrest.

"It's just like you giving a weapon to a police officer," Ercole said. "You would hope that he uses it properly and doesn't use it improperly and remembers his training. It's a good tool if used appropriately and with caution."

"It's just like you giving a weapon to a police officer. You would hope that he uses it properly."

Clearview's propensity to hand out free trials to officers using police department or government email addresses has sometimes created situations in which law enforcement agencies appear to have no idea the tool is being used by their employees. While the nation's largest police department, the NYPD, previously denied it had any formal relationship with Clearview, the document shows that officers there have run more than 11,000 searches, the most of any entity on the document. More than 30 officers have Clearview accounts, according to the logs.

An NYPD spokesperson told BuzzFeed News that while it does not have any contract or agreement with Clearview, its "established practices did not authorize the use of services such as Clearview AI nor did they specifically prohibit it."

"Technology developments are happening rapidly and law enforcement works to keep up with this technology in real time," the spokesperson said in a statement. "We are in the process of updating the NYPD's policy on Facial Recognition practices to address emerging issues."

Garvie said that these rogue uses of facial recognition are very concerning and that the public has no way of knowing whether all the searches served a law enforcement purpose.

"Not only are these officers operating completely outside of the established outside procedures set up by the NYPD to run these face recognition searches, but they're vastly expanding the types of cases to which face recognition is actually being applied," Garvie said.

Even when a police department decides Clearview is not the right fit, it can be hard to prevent officers from using it. The Raleigh Police Department in North Carolina was a paying client but later discontinued its relationship with the startup and put a moratorium on its use of the app after it was unable to get the company to fully comply with an audit.

Despite the severing of that relationship, Raleigh police officers continued to use Clearview beyond the ban on Feb. 11 and signed up with free trials, according to a department spokesperson.

Clearview isn't only targeting police departments at the state level. Multiple state government agencies are working with the company, according to its logs, including the Illinois secretary of state. Behind the NYPD, it's run the most searches of any entity on the list, clocking nearly 9,000 scans. A representative for the secretary's office did not respond to multiple requests for comment.

Clearview's client list also extends to the American education system, with more than 50 educational institutions across 24 states named in the log. Among them are two high schools.

Those two, Central Montco Technical High School in Pennsylvania and Somerset Berkley Regional High School in Massachusetts, did not respond to a request for comment. Somerset Police Department, which appears on the list with Somerset Berkley Regional, initially denied ever using Clearview or any facial recognition software, but later stated that a detective had received a 30-day free trial. The documents show that each school was only associated with one account. Neither had run more than five searches.

While most universities listed on the documents showed low search counts like the University of Alabama (about 30 searches) or the police at Florida International University (more than 200 searches), the fact that it was being used by officers or officials on campuses at all alarmed activists. In some cases, school officials had no idea it was being used.

"This is exactly why we've been calling for administrators to enact a ban," said Evan Greer, deputy director of Fight for the Future, a digital rights advocacy group. "So much of this happens in secrecy. A security officer shouldn't be able to use this to stalk students around campus."

A spokesperson for New York's Columbia University, which had one account listed that performed over 30 searches on the list and has similarly committed to not using facial recognition, told BuzzFeed News that "Columbia's Public Safety has never tested facial recognition technology and there are no plans to use it." They declined to say why someone associated with the university had tried Clearview.

Southern Methodist University first said that campus police were not using the software, but after multiple follow-ups from BuzzFeed News, a representative admitted that Clearview provided an employee with a test account. "SMU decided not to go forward with it," an official said, declining to answer further questions about why documents reviewed by BuzzFeed News showed multiple accounts tied to the university.

The University of Minnesota, which had previously committed to not using facial recognition, seemed to have a similar problem after documents showed that employees associated with the campus police department had used Clearview. A university spokesperson told BuzzFeed News that its police department "does not have a contract with Clearview AI."

"While some individual officers may have been offered trials of the software in the past, use of the program was not and is not part of regular business operations," said the spokesperson.

More than 200 companies have Clearview accounts, according to the documents, including major stores like Kohl's and Walmart and banks like Wells Fargo and Bank of America. While some of these entities have formal contracts with Clearview, the majority - as with public sector entities - appear to have only used the facial recognition software on free trials.

Greer said that if people focus conversations about facial recognition only on government or law enforcement uses, they are "missing the bigger picture."

"The fact that their client list includes all these major corporations shows that private entities can also use this type of invasive technology in incredibly abusive ways," she said.

For a company that maintains its tools are for law enforcement, Clearview's client list includes a startling number of private companies in industries like entertainment (Madison Square Garden and Eventbrite), gaming (Las Vegas Sands and Pechanga Resort Casino), sports (the NBA), fitness (Equinox), and even cryptocurrency (Coinbase).

"While we conducted a limited test as we do with an array of potential vendors, we are not and have never been a client of this company," an NBA spokesperson told BuzzFeed News. A representative for Madison Square Garden told BuzzFeed News after this story's publication that the venue demoed the product last year, but didn't move forward with a trial. Clearview's logs show that two accounts associated with the sports and events venue ran more than 70 searches at the end of 2019.

A spokesperson for Coinbase said the company was testing Clearview because of its "unique needs around security and compliance," but it was not using the service with customer data. "Our security and compliance teams tested Clearview AI to see if the service could meaningfully bolster our efforts to protect employees and offices against physical threats and investigate fraud," they said. "At this time, we have not made any commitments to use Clearview AI."

The logs also show the facial recognition startup is particularly interested in banking and finance, with 46 financial institutions trying the tool.

A Bank of America spokesperson confirmed to BuzzFeed News that it's not a paying customer, but declined to explain why Clearview's logs list it as having conducted more than 1,900 searches. "We're not a client of Clearview," a Bank of America spokesperson said. "We haven't been a client, we didn't stop being a client, and we never were a client."

Employees at big-box retailers, supermarkets, pharmacy chains, and department stores have also trialed Clearview. Company logs reviewed by BuzzFeed News include Walmart (nearly 300 searches), Best Buy (more than 200 searches), grocer Albertsons (more than 40 searches), and Rite Aid (about 35 searches). Kohl's, which has run more than 2,000 searches across 11 different accounts, and Macy's, a paying customer that has completed more than 6,000, are among the private companies with the most searches.

Employees at mobile carriers like AT&T, Verizon, and T-Mobile also appear in the Clearview documents. None of these companies appear to be paying customers, but their employees are listed as having collectively run hundreds of Clearview searches. AT&T, which searched for some 200 people, confirmed to BuzzFeed News that the company did not pay for the service, but declined further comment.

Clearview's code of conduct states that individual users must be "authorized by their employer" to use the tool, but that seems to be more of a guiding principle than an enforceable rule. Clearview's documents show that at Home Depot, five accounts ran nearly 100 searches.

"We don't use Clearview AI," a Home Depot representative told BuzzFeed News when asked for comment. "Curious why you thought we're a client."

Garvie was alarmed by Clearview's application to retail settings, noting that it could lead to the profiling of customers for shoplifting or theft.

"We don't use Clearview AI. Curious why you thought we're a client."

"That to me is a concerning premise because not only is there a complete absence of transparency into who gets suspected of shoplifting, and whether there's any redress provided to an individual," she said.

The documents reviewed by BuzzFeed News also indicate that the company has provided its software to private investigators and security firms. Among them is Gavin de Becker and Associates, a private security agency, which appears as a paid Clearview customer with more than 3,600 searches, and SilverSeal, a New York firm that engages in private investigation and surveillance, according to its website. Neither firm responded to requests for comment.

When BuzzFeed News reported earlier this month that Clearview AI had used marketing materials that suggested it was pursuing a "rapid international expansion," the company was dismissive, noting that it was focused on the US and Canada.

The company's client list suggests otherwise. It shows that Clearview AI has expanded to at least 26 countries outside the US, engaging national law enforcement agencies, government bodies, and police forces in Australia, Belgium, Brazil, Canada, Denmark, Finland, France, Ireland, India, Italy, Latvia, Lithuania, Malta, the Netherlands, Norway, Portugal, Serbia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.

The log also has an entry for Interpol, which ran more than 320 searches. Reached for comment, the worldwide policing agency confirmed that "a small number of officers" in its Crimes Against Children unit had used Clearview's facial recognition app with a 30-day free trial account. That trial has now ended and "there is no formal relationship between Interpol and Clearview," the Interpol General Secretariat said in a statement.

It's unclear how Clearview is vetting potential international clients, particularly in countries with records of human rights violations or authoritarian regimes. In an interview with PBS, Ton-That said Clearview would never sell to countries "adverse to the US," including China, Iran, and North Korea. Asked by PBS if he would sell to countries where being gay is a crime, he didn't answer, stating once again that the company's focus is on the US and Canada.

Clearview, however, has already provided its software to organizations in countries that have laws against LGBTQ individuals, according to its documents. In Saudi Arabia, for example, the documents indicate that Clearview gave access to the Thakaa Center, also known as the AI Center of Advanced Studies, a Riyadh-based research center whose clients include Saudi Arabia's Ministry of Investment. Thakaa, which did not respond to a request for comment, was given access to the software earlier this month, according to the documents.

In the UAE, which criminalizes homosexuality, the company's logs show that Clearview has provided its software to two entities, including Mubadala Investment Company, the country's sovereign wealth fund, which has run more than 100 searches. The facial recognition software has also been used by UAE police, according to the documents, which indicate that it's specifically used for the Ministry of Interior's Child Protection Center in Abu Dhabi.

Outside of the US, Clearview's largest market is Canada, where company logs show access to its app has been given to both public and private entities. There are more than 30 law enforcement agencies in the country with access to the software, including the Royal Canadian Mounted Police, which is listed as a paying customer according to the documents, and the Toronto Police Service, which despite being on free trials have run more than 3,400 searches across about 150 accounts.

Just as in the US, some law enforcement agencies around the world seemed unaware that their officers or employees had signed up and used Clearview. The Australian Federal Police said in a statement that it does not use it but declined to comment on why Clearview's records show that employees associated with the organization have run more than 100 searches - some as recently as January 2020. In the UK, London's Metropolitan Police only told BuzzFeed News that Clearview was not being used in its recently deployed live facial recognition tool but declined to comment on the more than 170 searches noted in Clearview's logs.

Some responses were more ominous. In India, the only entity that has signed up for the software was the Vadodara City Police in the western state of Gujarat. The startup's records show that the department signed up last month and had only run a handful of searches. When asked by a BuzzFeed News reporter if police in the city were still using the facial recognition technology, Police Commissioner Anupam Singh Gahlaut responded with a short text and did not respond to further questions.

"We have not started yet." ●
Hannah Ryan in Sydney, Emily Ashton in the United Kingdom, and Pranav Dixit in Delhi contributed reporting to this story.

# data[].attributes.attachment_link.title
Clearview's Facial Recognition App Has Been Used By The Justice Department, ICE, Macy's, Walmart, And The NBA

# data[].attributes.attachment_link.description
The United States' main immigration enforcement agency, the Department of Justice, retailers including Best Buy and Macy's, and a sovereign wealth fund in the United Arab Emirates are among the thousands of government entities and private businesses around the world listed as clients of the controversial facial recognition startup with a database of billions of photos scraped from social media and the web.

# data[].attributes.headline
Before Clearview Became a Police Tool, It Was a Secret Plaything of the Rich

# data[].attributes.subject
Before Clearview Became a Police Tool, It Was a Secret Plaything of the Rich

# data[].attributes.body
Investors and clients of the facial recognition start-up freely used the app on dates and at parties — and to spy on the public.

https://www.nytimes.com/2020/03/05/technology/clearview-investors.html

# data[].attributes.attachment_link.title
Facebook reveals news feed experiment to control emotions

# data[].attributes.attachment_link.description
Protests over secret study involving 689,000 users in which friends' postings were moved to influence moods

# data[].attributes.body
A brief explanation of the Cambridge Analytica scandal.

# data[].attributes.attachment_link.title
Facebook's Cambridge Analytica data scandal, explained

# data[].attributes.body
The "Color of Surveillance" Reading List

(I bumped up this post from a month ago)

# data[].attributes.headline
Banjo: Surveillance + Predictive Policing

# data[].attributes.subject
Banjo: Surveillance + Predictive Policing

# data[].attributes.body
This might be of interest to anyone wanting to research surveillance, policing, or the criminal justice system.

# data[].attributes.attachment_link.readable_text
The state of Utah has given an artificial intelligence company real-time access to state traffic cameras, CCTV and "public safety" cameras, 911 emergency systems, location data for state-owned vehicles, and other sensitive data.

The company, called Banjo, says that it's combining this data with information collected from social media, satellites, and other apps, and claims its algorithms "detect anomalies" in the real world.

The lofty goal of Banjo's system is to alert law enforcement of crimes as they happen. It claims it does this while somehow stripping all personal data from the system, allowing it to help cops without putting anyone's privacy at risk. As with other algorithmic crime systems, there is little public oversight or information about how, exactly, the system determines what is worth alerting cops to.

In its pitches to prospective clients, Banjo promises its technology, called "Live Time Intelligence," can identify, and potentially help police solve, an incredible variety of crimes in real-time. Banjo says its AI can help police solve child kidnapping cases "in seconds," identify active shooter situations as they happen, or potentially send an alert when there's a traffic accident, airbag deployment, fire, or a car is driving the wrong way down the road. Banjo says it has "a solution for homelessness" and can help with the opioid epidemic by detecting "opioid events." It offers "artificial intelligence processing" of state-owned audio sensors that "include but may not be limited to speech recognition and natural language processing" as well as automatic scene detection, object recognition, and vehicle detection on real-time video footage pulled in from Utah's cameras.

In July, Banjo signed a five-year, $20.7 million contract with Utah that gives the company unprecedented access to data the state collects. Banjo's pitch to state and local agencies is that the more data that's fed into it, the better its product will work. Thus, the company has spent the last year trying to get as many state and local agencies as possible to give it access to its CCTV and traffic cameras, audio sensors, and other data.

"We essentially do most of what Palantir does, we just do it live"

On this it has been incredibly successful. Banjo has installed its own servers in the headquarters of the Utah Department of Transportation (UDOT), a civilian agency, and has direct, real-time access to the thousands of traffic cameras the state operates. It has jacked into 911 systems of emergency operations centers all over the state, according to contracts, emails, and other government documents obtained by Motherboard using public record requests, as well as video and audio recordings of city council meetings around the state that we reviewed.

Its contract with the state says that Banjo's technology will be deployed or is in the process of being deployed in all 29 of Utah's counties, in the state's 13 largest cities, and in 10 other cities with "significant relevance" as well as for "campus security" for the University of Utah. A representative for Banjo told the city of Springville, Utah in January that a total of roughly 70 other cities and counties within Utah had agreed to give Banjo their data. It is also working with the Utah Department of Public Safety and the Utah Highway Patrol, according to public records.

So far, however, the Utah Attorney General's office could not provide Motherboard with an example of a real-life case in which Banjo has been used, though it insisted that it had indeed been used.

Banjo is yet another in a new crop of predictive policing and artificial intelligence-focused policing tools. Unlike other tools, such as Palantir- which is used by ICE and is so controversial that there have been multiple protests about its use -Banjo claims that it does not help police find criminals, it helps them to find "emergencies" and locate criminal acts.

"We essentially do most of what Palantir does, we just do it live," Bryan Smith, Banjo's top lobbyist, told the Salt Lake Valley Emergency Communications Center Operations Board, which is made up of police chiefs and 911 dispatch officials, in August. "So Palantir is a tool you use for analysis, kind of to deep dive investigate certain things. What we want to do is deliver you the information right at the moment. Think of a Google Maps [you'll get] a pin that drops with a [computer-aided dispatch] caller and attached to that event, within sub one second with intelligence related to that event."

"We don't have enough eyes to watch all the cameras, we don't have enough people to understand all of the different signals, so what we use the system to understand those signals"

At the meeting, Smith aimed to get Banjo access to 911 call information, which the company has since received, according to emails with local police. He said that "we strip out all personally identifiable information. We're not trying to ID a suspect at all. What we're trying to do is point you guys to a problem. We're telling you where the needle in a haystack is, not who that problem is."

What remains unclear is how a company that proposes to tell police of "anomalies" as they happen could protect people's privacy, or operate without serious biases, two largely intractable problems with surveillance and AI.

Banjo also proposes to monitor thousands of public surveillance cameras in real time with an algorithm rather than a human, something that has terrified security and civil liberties experts for years.

"We don't have enough eyes to watch all the cameras, we don't have enough people to understand all of the different signals, so what we use the system to understand those signals," Smith said at the meeting.

Privacy experts are unsure how Banjo can be doing anything other than applying machine learning to a terrifying amount of data to create a persistent panopticon pointed at everyone who lives in Utah.

"I've run out of adjectives to describe how upsetting and dystopian things like this are," Chris Gilliard, a professor at Macomb Community College who studies police surveillance and privacy, told Motherboard after reviewing parts of Banjo's contracts with the state.
Do you work at Banjo or know anything else about the company's work? We'd love to hear from you. Using a non-work phone or computer, you can contact Jason Koebler securely on Signal on +1 202 505 1702 , or Joseph Cox on Signal on +44 20 8133 5190 , Wickr on josephcox, OTR chat on jfcox@jabber.ccc.de , or email joseph.cox@vice.com.
"This rise of video analytics, of being able to not just monitor but analyze everything that's going on in overwhelming streams of data is a game-changing power for police," Andrew Ferguson, author of The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, told Motherboard. "The ability to see and categorize and understand everything is something we've never had before. AI-infused eyesight is a superpower that we haven't seen before in policing, and is something we really should have rules about and discuss whether that's OK."

Utah fits well with the image Banjo CEO Damien Patton has cultivated. He's more rugged and wholesome than your average Silicon Valley CEO. He's a former NASCAR pit mechanic, a "one-time crime scene investigator," and did two tours in Operation Desert Storm during his time in the Navy. He races Banjo-branded trophy trucks through the Utah desert, rides motorcycles, and in the last few years he's grown what can only be described as a ZZ Top beard. He wears camouflage suit jackets to public speaking events.

Banjo originally had no connection to Utah, but its business is now intertwined with the state. It moved its headquarters from Las Vegas to Park City in 2018. Smith, its chief lobbyist, is the former head of Utah's Republican party and a former Chief of Staff in Utah's State Senate. Both the Utah Office of Economic Development and the Economic Development Corporation of Utah have published interviews with Patton that have largely focused on how Utah is a nice place to run a business: "I can't imagine any better place than Utah," Patton said in one of the interviews.

In 2008, at the age of 35, Patton was clean shaven and had just founded a FindAnyFloor.com, a flooring "information site and social community." Patton's bearded, trail-blazing persona has become a core part of Banjo's story.

"He looks like a guy you think would be playing a banjo," Smith told the city council of Lehi, Utah, in December while pitching the company at a council meeting. "He's not your typical tech CEO."

Patton has told Banjo's origin story many times. As his story goes, he tweeted about waiting for a flight back to his home in Las Vegas while at Boston Logan International Airport. When he got home, he saw that his friend, who served with him in Desert Storm and who he hasn't seen in 15 years, posted on Facebook about being at Logan at the same time. Both friends were at the same place, at the same time, and said so on social media, but still managed to miss each other like ships in the night.

Banjo was originally designed to fix this missed opportunity. By scanning public social media feeds and comparing them with your interests, the app's aim was to let users know when their friends are nearby, if their favorite band happens to be playing in the city they're traveling to, and so on.

The company trudged along like this for a few years-a location-based social media aggregator for consumers. According to Patton, the company's plans changed entirely on April 15, 2013, the day of the Boston Marathon bombing.

"The moment that bombing happened, none of us knew it was a bombing. We knew it was an explosion. I went immediately into our system and said, 'Can I rewind time and go back to that location where that explosion was and know what happened before the explosion occurred?'" Patton said.

"In that moment in time I discovered literally what had happened before the explosion went off, who the people were around that, when the explosion went off, what was happening, and that it was a bomb. I went to the [company] board that day and said 'the company is changing.' If we could automate-if we could build AI that could have known this ahead of time, and certainly know this in real-time, imagine how that could have changed everything. At that moment our mission changed and from that moment on we've been building the crystal ball."

(Peter Thiel's surveillance and big data analytics company Palantir, incidentally, is named after a crystal ball from Lord of the Rings, which allows the person who wields it to see any part of the world, past or future.)

Plenty of companies, such as Geofeedia, have sold tools to law enforcement that scrape social media to alert or inform police on what is happening in a particular area. But Banjo differs in that it not only uses social media posts, but also has direct access to state data, such as camera feeds.

"Twitter shut down Geofeedia's access and they shuttered themselves. Even public-focused social media with geolocational technologies could be invasive to associational freedoms, privacy and the rest. This seems to be that technology on steroids," Ferguson said.

Banjo did not respond to four emailed requests for an interview sent to Patton, Smith, another executive, and its general inbox. They also did not respond to a detailed list of questions for this article, which asked for more specifics about its anonymization process and patents, statistics about Banjo's use in Utah, any examples of when it had been used in a real-life situation, its data access and protection protocols, and any work it has done outside Utah.

"Our feeling is the best way to predict the future is to invent it," Ric Cantrell, chief of staff for Utah's Attorney General, said in an interview. "What if we could invent it in a Utah-specific way that, on one hand, understands that need for law enforcement to have powerful tools to do their job, but on the other hand respects the right of every American citizen to believe what they want, to say what they want, to associate how they want, to be free from government intrusion?"

Emails obtained via public records request between Banjo and Utah officials show how the company pitches its services to agencies across the state. In one instance, Patton himself tries to ingratiate himself to police chief Ken Wallenstine around a hobby they share: riding motorcycles. In an email to the chief of the West Jordan, Utah police department, Patton wrote: "Happy December - which only means you are somewhere warm riding right now. I wish I was there! For [Salt Lake County], there are only a couple of people that aren't on our radar yet. One is the chief of South Salt Lake. I heard he likes to ride, so I am going out on a limb here and seeing if you know him well. I would like to get our team over there and showing him Banjo."

In January, Wallentine emailed Patton to ask if he was "at all interested in naked land for building" in the city. "I chatted to our economic development guy and he might have some space ideas. We'd love to have Banjo in West Jordan!" A public information officer for the city of West Jordan told Motherboard that it is "aware" of the company but that it is "not utilizing Banjo as part of its public safety operations," though the city's police department and mayor signed an agreement giving the company access to its police dispatch system, public safety cameras, and other city data in January. On January 30, Smith emailed officials in the city to "coordinate on implementation of your signals and starting to schedule trainings for your officers."

In another email to Gary Giles, chief of police in Orem, Utah and president of the Utah Chiefs of Police Association, Smith appears to offer him help while pitching him on Banjo's services.

"I just wanted to check in with you here at the start of the year on getting together about getting Banjo for you and your team," Smith wrote in a January 9 email to Giles. "Do you and/or your command staff have any times in the near future we could get together?"

"I am limited on when I can do it as the legislative session is coming up and I am going to be in Salt Lake quite a bit," Giles wrote back.

"I totally understand about the legislative session - Congrats by the way," Smith replied. "I was the Deputy Chief of Staff in the Utah Senate before jumping over here to Banjo. I'd also love to chat about how I could support what you are doing up on this hill this session."

Giles told Motherboard that he did not talk to Banjo about any legislation. "My conversations with Banjo have never been about anything except them wanting to give us a presentation," he said. "Other than meeting with Bryan at Orem PD (I believe in January), I have not spoken with him."

While Banjo is based in Utah and its website lists three offices in the state, the company also has offices in Redwood City, California; Washington, D.C.; and Las Vegas. The company's largest and most advanced presence appears to be in Utah, though Banjo has mentioned in presentations to police that it also works in Arizona. According to Crunchbase, it has raised over $120 million from investors, most of which came from a $100 million Series C round of investments led by Softbank, which famously invests in WeWork, Uber, and a host of other startups from its $100 billion "Vision Fund." Banjo now claims to hold one of the largest "live data stores" in the world.

What it's doing with that data, other than amassing a lot more of it, is still not clear.

Patton talks regularly about ethics in AI. While speaking to students at Stanford in January 2019, Patton said that unlike his contemporary CEOs in Silicon Valley who collect and monetize private data with advertising, he believes tech can be "altruistic." He explains that Banjo collects data in order to save lives, and that it's able to do so while keeping people anonymous and protecting privacy.

"From day one we've stripped personal identifiable information (PII) from all of our data, where even our engineers cannot deanonymize it," Patton said at a conference last year. Its stated mission is to "reduce human suffering."

Banjo's pitch to Utah from the beginning has been that it finds crime without identifying criminals. It looks for cars without looking for who is inside the cars. It detects riots and protests without telling cops who is at them. It detects drug use hotspots without identifying the drug user. Patton often mentions his company has patented technology to strip PII from publicly available data, but patents Motherboard found do not go into detail about how that is done.

Ferguson said it's at least laudable that the company is even mentioning privacy: "I do think a company should get some credit for trying to take some steps to recognize the risks of personal identifiable information in their system and trying to limit the privacy-invading nature of the privacy-invading surveillance technology it's building."

Cantrell, of the Utah Attorney General's office, said that Banjo is one of the Utah startup scene's crown jewels, and that its policy on privacy is what convinced him to set up a meeting.

"One of the founders of the Silicon Slope movement called our office and said, 'Hey, I just met a dude, and what he is engaged in is pretty phenomenal. You need to come meet him.' And so we did," Cantrell said. "it became pretty apparent that this emerging technology could help us in our fundamental constitutional and statutory responsibilities to keep the people of Utah safe. We respected the company because of their careful attention to keep private data private."

In 2018, Cantrell's office set up a child abduction drill with law enforcement in the state and invited Banjo to participate. Smith later described the drill to the city council of Lehi, Utah, in a December 2019 presentation. In that drill, police officers-a "staff of 100," Smith said-took eight hours to recover the simulated child. "A stranger abduction at eight hours, you're looking for a body at that point more than a person," Smith said. Banjo, he said, located the child in 27 seconds "and we could have helped them recover the child in four minutes."

Smith didn't explain how Banjo was able to locate the simulated child so quickly, but during his presentation he shows a powerpoint slide that has dozens of data points feeding into its system: "social media, 911 calls, weather, utilities, air quality, public cameras, traffic cameras, emergency services, autonomous vehicles, flight info, public cameras." In its contract with the state, it also says it can do "data signal ingestion for IoT Sensors," but does not elaborate. Banjo also claims to have a partnership with Waze, the Google-owned navigation app which tracks traffic with phone location data. (Waze did not respond to a request for comment.)

In his demo, the only data that has any names attached, Smith says, is the child sex offender registry, "because that's public information." Smith's presentation to Lehi was intended to (and did) convince the city council to share access to its own public safety and CCTV cameras, automatic vehicle location for city-owned cars, its record management system (with PII removed), and its computer-aided dispatch system with the company, which would in turn make both the city and the entire state safer. The city council adopted a resolution to work with Banjo a week after his presentation, and the city officially signed its contract at the end of January, according to an executed contract obtained using a public records request.

In return for cities' data, Banjo says it will send alerts to cops when there's an "emergency," giving them the location and time of the incident as well as information about that incident. Police officers who are trained to use Banjo can also log into a web portal and look at a specific area to see what types of signals Banjo is collecting. They can also look at a map and "rewind time" to look at the locations of public transportation, CCTV cameras, and look at other data Banjo ingested at that time.

The closest Banjo gets to describing exactly how its technology works and what it will do with that data is related to its supposed processing of the 2017 Las Vegas shooting at Mandalay Bay.

"Literally before law enforcement was on the scene, we were able to detect there was a mass incident going on and where it was at, based on a variety of signals in the area," Smith told the Lehi city council.

He said the company had parsed a Facebook Live video in which "a gentlemen in Spanish said something about gunshots. We don't know who that person was." Meanwhile, a mile from where that Facebook Live video was being shared, camera footage Banjo ingested suggested there was a "riot" occurring on the Las Vegas Strip. "But it wasn't a riot, it was just people running in patterns across the road in a way they don't normally do. That, in combination with a tweet, we were able with confidence to say a mass shooting was happening. Sadly, we weren't working with law enforcement at the time." Las Vegas Metropolitan Police said in response to a public records request that it has never had a formal conversation with Banjo.

In his demo to Lehi, Smith explained how the company now has its servers within the Utah Department of Transportation's headquarters, and that it is now collecting and analyzing every traffic camera it operates: "Their cameras have never been able to be used for law enforcement or public safety despite having over a thousand of them," he said.

Smith explained that Banjo's AI can look for specific types of cars on UDOT camera footage: "We're helping law enforcement find the child in this event, not the perpetrator. We're not helping to lock them up or criminalizing anyone. We're not. [...] We're not looking for people or gender. It's cars. They're UDOT cameras, they're transportation cameras, they're made for cars, so we're going to look for cars."

"Again, this is specific to a child abduction," he added.

Emails and contracts obtained by Motherboard from the Utah Department of Transportation using a public records request show that Banjo's initial contract with UDOT allowed for the platform's use only in "child abduction cases." This contract was amended in December to allow for all "emergency event detection," according to emails between the Utah Attorney General's office and the Department of Transportation, this request came at the request of the Utah Department of Public Safety. The Utah DPS said in an email that it currently "uses Banjo for traffic monitoring and response and not for any criminal investigations."

"If they are drawing inferences based on information collected from, say, security cameras, then they are going to be more likely to notice events that happen where security cameras have been placed"

The emails also confirm that Banjo has installed its own server infrastructure behind UDOT's firewall. Before Banjo, the UDOT never saved video from its cameras and instead relied on them to give real-time traffic updates. Banjo is now recording the footage and making it available for 24 hours, after which it will be deleted, according to the contract, emails, and presentations Smith has given.

Cantrell, of the Utah Attorney General's Office, said that his office and state police are primarily focused on the possible use of Banjo in a child abduction case, but noted that Banjo has never been used for that purpose and that there has not been a "major kidnapping event" in Utah in the last two years.

"If a kidnapping occurs in certain areas where Banjo is now covering, we have hope that we could bring a child home safe where we wouldn't have been able to in the past," Cantrell said. "If a child gets kidnapped, and I'm painting with a broad brush, but generally, statistically, it's going to be an 11-year-old girl, it's going to be ... I don't want to get too gruesome."

There is a long history of surveillance products being sold to the public using worst-case scenarios-child kidnappings, terrorism, mass shootings-and then quickly trickling down to be used for more mundane purposes. Previously sold to mostly law enforcement agencies, schools now use facial recognition technology. IMSI-catchers, which track cell phones' locations, are now used to investigate a wide range of crimes. iPhone cracking technology, once the center of a federal standoff between the FBI and Apple in the aftermath of the San Bernardino mass shooting, is now regularly sold to state and local cops.

The cases in which Banjo could be used has already been expanded. After initially being interested in child abductions, Cantrell's office has also signed up for Banjo's "opioid module," which the Utah Department of Health told the Utah state legislature was developed for $250,000 as part of a U.S. Drug Enforcement Administration program.

Banjo's opioid module, according to the Utah Department of Health, is designed to be a "live heat map and trend analysis of opioid-related events." The module will "provide agencies, such as the DEA, Attorney General's Office, and law enforcement entities within Salt Lake County with live insight into the crisis so proactive and immediate support resources can be effectively deployed." Public documents reviewed by Motherboard don't explain what data Banjo uses for this system, but Cantrell said that it will take into account "emergency room visits, morgue visits, naloxone use, which beds are open and where for addiction recovery."

Dr. Daniel Ciccarone, a professor at the University of California, San Francisco, who studies the heroin and opioid epidemic, told Motherboard he is skeptical of this plan because "they're not going to have very good data," and because the organizations that will actually receive the information are law enforcement, not harm reduction specialists. "What's the purpose of the AI? How will it help? Is it to make the criminal justice system harsher? We're starting to see things like that elsewhere, and it's UNPRODUCTIVE, capital letters, underscore, bold font," he said. "I would be very worried about any technology set to give information to law enforcement for the purposes of public safety."

Anne Boustead, an assistant professor at the University of Arizona who studies electronic surveillance and drug policy, said the data is likely to be skewed: "I have concerns about whether a tool such as this would provide an unbiased picture of opioid use in Utah. If they are drawing on data is disproportionately gathered from certain places/people, then they are going to be more likely to notice when an opioid event (and to be honest, I'm not really sure what that means?) occurs in those places," she said.

"If they are drawing inferences based on information collected from, say, security cameras, then they are going to be more likely to notice events that happen where security cameras have been placed," she added. "If these are located in socioeconomically disadvantaged communities, then it could lead to inferences that opioid events are more likely to occur in these communities-when, in fact, it is just more likely that this will be observed."

There is, again, almost no information about how Banjo actually works, what its algorithm considers to be an "anomaly" or an "emergency event," and how it will be deployed.

"Do people implicated under [Banjo's programs] have any means of examining it or calling it into question?" Gilliard said. "Will people who might be potentially exonerated by a system be able to request information and documents? Usually the answers to these questions are 'no.'"

Banjo will, again, direct police to the anomalies and emergency events it detects in real time. Even if all names are stripped from the data, there will be real people at the other end. Maybe that person will be a kidnapper or mass shooter. Maybe it will be a truant student, or a drug user, or a protester. These people will, inevitably, be forced into potentially dangerous interactions with the police.

"It's only a small step from going to predicting something that might happen, and figuring out a way to cause something to happen," Dave Maass, a senior investigative researcher at the Electronic Frontier Foundation, told Motherboard. "I do not believe anything that a company offering predictive systems tells me anymore ... I don't even know how you'd show efficacy."

Patton, for his part, says that there is only one statistic that matters to the company:

"We measure only one KPI [key performance indicator] at Banjo. Truly one KPI across the board, and that's lives saved," he said.

What is unclear is how many lives Banjo has saved, if it has saved any, and whether it's even possible to create a product that automatically detects crime without creating a creeping surveillance state that inherently infringes on people's rights and invades their privacy.

While it's still very early days for its implementation in Utah, we have no idea whether it has been useful in the real world. Cantrell couldn't identify a single case that Banjo's technology had helped on. A public records request sent to Utah's Highway Patrol requesting any case reports in which Banjo was used returned no documents. Police departments who have signed up for Banjo told Motherboard that they have not actually used it. This doesn't mean, of course, that Banjo has never been used or that it's not been helpful, it's just that neither the company nor the state of Utah has been able to produce any evidence that it has.

"They do have case studies," Cantrell said. "I'm waiting for case studies from Banjo. I'm still waiting for information from them."

Cantrell suggested there is a bright future ahead for Banjo. The state is exploring a "homelessness module," and that if it wanted to, it could add a "meth module" or a "human trafficking" module. A homelessness module would "chart beds open at homeless shelters, spikes in crimes, 911 calls, things like that." Cantrell said you can never really know what the technology might be useful for. Police officers in West Jordan have openly mused in emails about getting a government grant to deploy Banjo in schools.

"In the next 50 years there's going to be incredible emerging technologies that law enforcement, governments, citizens have never thought of before, and we want to make sure that we utilize appropriate technology to keep people safe," Cantrell said. "Which is our mission, right?"

# data[].attributes.attachment_link.title
This Small Company Is Turning Utah Into a Surveillance Panopticon

# data[].attributes.attachment_link.description
The state of Utah has given an artificial intelligence company real-time access to state traffic cameras, CCTV and "public safety" cameras, 911 emergency systems, location data for state-owned vehicles, and other sensitive data. The company, called Banjo, says that it's combining this data with information collected from social media, satellites, and other apps, and claims its algorithms "detect anomalies" in the real world.

# data[].attributes.body
Waymo is an American autonomous driving technology development company. Google began this project back in 2009, and in December of 2016 Waymo became a stand-alone company. Then in April 2017, the company stated a limited trial of a self-driving taxi service in Phoenix, Arizona. Today, March 4, 2020, Waymo unveiled its fifth-generation Waymo Driver, the combination of hardware and software that guides its autonomous vehicles.

Video:

Behind the Waymo Driver

# data[].attributes.attachment_link.readable_text
SAN FRANCISCO (Reuters) - Amazon.com Inc's machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.

The team had been building computer programs since 2014 to review job applicants' resumes with the aim of mechanizing the search for top talent, five people familiar with the effort told Reuters.

Automation has been key to Amazon's e-commerce dominance, be it inside warehouses or driving pricing decisions. The company's experimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars - much like shoppers rate products on Amazon, some of the people said.

"Everyone wanted this holy grail," one of the people said. "They literally wanted it to be an engine where I'm going to give you 100 resumes, it will spit out the top five, and we'll hire those."

But by 2015, the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way.

That is because Amazon's computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry. (For a graphic on gender breakdowns in tech, see: tmsnrt.rs/2OfPWoD)

In effect, Amazon's system taught itself that male candidates were preferable. It penalized resumes that included the word "women's," as in "women's chess club captain." And it downgraded graduates of two all-women's colleges, according to people familiar with the matter. They did not specify the names of the schools.

Amazon edited the programs to make them neutral to these particular terms. But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people said.

The Seattle company ultimately disbanded the team by the start of last year because executives lost hope for the project, according to the people, who spoke on condition of anonymity. Amazon's recruiters looked at the recommendations generated by the tool when searching for new hires, but never relied solely on those rankings, they said.

Amazon declined to comment on the technology's challenges, but said the tool "was never used by Amazon recruiters to evaluate candidates." The company did not elaborate further. It did not dispute that recruiters looked at the recommendations generated by the recruiting engine.

The company's experiment, which Reuters is first to report, offers a case study in the limitations of machine learning. It also serves as a lesson to the growing list of large companies including Hilton Worldwide Holdings Inc and Goldman Sachs Group Inc that are looking to automate portions of the hiring process.

Some 55 percent of U.S. human resources managers said artificial intelligence, or AI, would be a regular part of their work within the next five years, according to a 2017 survey by talent software firm CareerBuilder.

Employers have long dreamed of harnessing technology to widen the hiring net and reduce reliance on subjective opinions of human recruiters. But computer scientists such as Nihar Shah, who teaches machine learning at Carnegie Mellon University, say there is still much work to do.

"How to ensure that the algorithm is fair, how to make sure the algorithm is really interpretable and explainable - that's still quite far off," he said.

MASCULINE LANGUAGE

Amazon's experiment began at a pivotal moment for the world's largest online retailer. Machine learning was gaining traction in the technology world, thanks to a surge in low-cost computing power. And Amazon's Human Resources department was about to embark on a hiring spree: Since June 2015, the company's global headcount has more than tripled to 575,700 workers, regulatory filings show.

The logo of Amazon is seen at the company logistics centre in Boves, France, August 8, 2018. REUTERS/Pascal Rossignol/Files

So it set up a team in Amazon's Edinburgh engineering hub that grew to around a dozen people. Their goal was to develop AI that could rapidly crawl the web and spot candidates worth recruiting, the people familiar with the matter said.

The group created 500 computer models focused on specific job functions and locations. They taught each to recognise some 50,000 terms that showed up on past candidates' resumes. The algorithms learned to assign little significance to skills that were common across IT applicants, such as the ability to write various computer codes, the people said.

Instead, the technology favoured candidates who described themselves using verbs more commonly found on male engineers' resumes, such as "executed" and "captured," one person said.

Gender bias was not the only issue. Problems with the data that underpinned the models' judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.

THE PROBLEM, OR THE CURE?

Other companies are forging ahead, underscoring the eagerness of employers to harness AI for hiring.

Kevin Parker, chief executive of HireVue, a startup near Salt Lake City, said automation is helping firms look beyond the same recruiting networks upon which they have long relied. His firm analyses candidates' speech and facial expressions in video interviews to reduce reliance on resumes.

"You weren't going back to the same old places; you weren't going back to just Ivy League schools," Parker said. His company's customers include Unilever PLC and Hilton.

Goldman Sachs has created its own resume analysis tool that tries to match candidates with the division where they would be the "best fit," the company said.

Microsoft Corp's LinkedIn, the world's largest professional network, has gone further. It offers employers algorithmic rankings of candidates based on their fit for job postings on its site.

Still, John Jersin, vice president of LinkedIn Talent Solutions, said the service is not a replacement for traditional recruiters.

"I certainly would not trust any AI system today to make a hiring decision on its own," he said. "The technology is just not ready yet."

Some activists say they are concerned about transparency in AI. The American Civil Liberties Union is currently challenging a law that allows criminal prosecution of researchers and journalists who test hiring websites' algorithms for discrimination.

"We are increasingly focusing on algorithmic fairness as an issue," said Rachel Goodman, a staff attorney with the Racial Justice Program at the ACLU.

Still, Goodman and other critics of AI acknowledged it could be exceedingly difficult to sue an employer over automated hiring: Job candidates might never know it was being used.

As for Amazon, the company managed to salvage some of what it learned from its failed AI experiment. It now uses a "much-watered down version" of the recruiting engine to help with some rudimentary chores, including culling duplicate candidate profiles from databases, one of the people familiar with the project said.

Another said a new team in Edinburgh has been formed to give automated employment screening another try, this time with a focus on diversity.

Reporting By Jeffrey Dastin in San Francisco; Editing by Jonathan Weber and Marla Dickerson

Our Standards: The Thomson Reuters Trust Principles.

# data[].attributes.attachment_link.title
Insight - Amazon scraps secret AI recruiting tool that showed bias against women

# data[].attributes.attachment_link.description
Amazon.com Inc's <AMZN.O> machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.

# data[].attributes.body
Cashier-less grocery store technology.

# data[].attributes.attachment_link.readable_text
SEATTLE - Amazon is cracking further into the $800 billion U.S. grocery industry, keeping the likes of Walmart, Kroger, 7-Eleven and Instacart on their toes.

The online behemoth on Tuesday is opening its first, full-size, cashierless grocery store. Five years in the making, the Amazon Go Grocery is in the Capitol Hill neighborhood of Seattle, in the Amazon corporate headquarters' backyard.

Amazon has been working on the space since 2015. At 10,400 square feet, the store at 610 E. Pike St. incorporates the same technology found in the two dozenor so Amazon Go locations. Shoppers can walk in, scan a QR code from their Amazon mobile app at a turnstile, carry or add whatever they want to their baskets throughout the store, and walk out when they are finished. Zero human interaction is required, though the store will staff a couple dozen people to help stock shelves and answer shoppers' questions.

"You're seeing a lot of big strides in [this] store," Cameron Janes, vice president of Amazon's physical retail division, told CNBC during a tour of the grocery store on Monday. "Produce is a big example of that."

"We're just getting started here," he said, declining to comment on how many of these grocery stores Amazon may open. "I think what we're trying to do here - and with all of our physical stores - is really work backwards from the customer, and deliver some differentiation."

The space in Seattle is still smaller than a typical U.S. grocery store that's about 40,000 square feet. Amazon Go Grocery is meant to stock shoppers' kitchen cabinets, and help them with dinner, while Amazon Go stores are meant to serve bustling business districts during the breakfast and lunch hours, Janes said. He called Amazon Go Grocery a "neighborhood market" of sorts.

The new store is stocked with about 5,000 items, including fresh produce, dairy, packaged seafood, meats, bakery treats like doughnuts, household goods like paper towels plus meal kits and a full liquor selection with wines and beer.

Some items, mainly in the produce department, are sourced from Whole Foods' providers. Items from national brands as well as a variety of Amazon's private labels, such as Happy Belly, are also for sale. The store includes a self-service coffee bar.

Every item is priced individually, meaning no weighing required for produce, for example. Bananas are 19 cents each and avocados are 49 cents.

Making sure Amazon's technology could track shoppers picking up and bagging their own produce in this store was the "biggest incremental challenge ... to enable customers just to shop and not have to worry about the technology," Janes said.

The Amazon Go Grocery store is meant to complement the "natural and organic" brands that people can find at Whole Foods today, he said. "We're not trying to be Whole Foods," Janes said. "We're not trying to replace them."

Amazon's first Go location, spanning about 1,800 square feet, finally opened to the public from the middle of Amazon's Seattle campus in January 2018. The space - selling grab-and-go items such as sandwiches and snacks - was initially unveiled in late 2016. It was supposed to open to the public in early 2017. But it was stuck for much longer in beta mode only for Amazon employees as the company worked to get the complicated cashierless technology right.

Since Amazon has taken its time to get its own cashier-free technology up to speed, rivals have had more time to swoop in and mimic it, analysts have said.

Two tech start-ups, AiFi and Grabango, for example, are working on autonomous systems for big retailers competing with Amazon.

Earlier this month, 7-Eleven, which has more than 11,000 convenience stores in North America, opened a 700-square-foot cashierless store near its corporate headquarters in Irving, Texas. The space is a pilot test and only open to employees, the company said.

Walmart's wholesale division has also been testing cashier-free technology at a store in Dallas, called Sam's Club Now.

Still, Amazon's efforts in grocery cannot be ignored. Its ambitions in the category were certainly amplified when it paid $13.7 billion in 2017 to buy the upscale grocery chain Whole Foods Market.

People still go to grocery stores, even with the rampant rise of online shopping, and Amazon knows that. Only about 3% of groceries are ordered on the internet today in the United States.

Because of its size and other business units, such as Amazon Web Services, analysts say Amazon is able to experiment more in retail than others without having to worry about profitability. Amazon has a market cap of more than $1 trillion. Its shares have rallied more than 20% in the past 12 months.

"Nobody in their right mind goes into grocery. ... It is low margins, with perishable food, and it's challenging to scale," said Brendan Witcher, a principal retail analyst with Forrester Research. "But there is nowhere we shop more than grocery stores. That is [Amazon's] strategy. It doesn't need to be a profit generator."

Amazon, with its more than 150 million paid Prime members globally, wants to get in front of as many consumers as possible, he said. "Amazon's primary mission is to get you to say 'Amazon' every day of your life. ... Amazon is the only one who can afford to make the entire grocery category a loss leader."

Amazon reportedly had been forecasting annual revenue from all Amazon Go stores would skyrocket from $28 million in 2018 to upwards of $639 million in 2020, according to The Information. But Amazon hasn't opened as many Go stores as it had initially anticipated, the report said. And operating losses from Go are still ballooning, it said.

Amazon declined to comment on these numbers.

"We believe we can be profitable," Janes told CNBC about Amazon's grocery business.

Amazon is also set to open a first-of-its-kind grocery store in the Woodland Hills neighborhood of Los Angeles, without its Go technology and not under the Whole Foods banner, at some point in 2020. Details about this new chain remain unclear, and Amazon's Janes declined to comment further.

"Amazon is taking the long game here," Witcher said. "I don't know a single retailer that could work on something for five years without a single return on investment, and keep going."

CNBC had also previously reported that Amazon could be looking to scale its cashier-free technology to other retailers and chains, such as movie theaters and airports.

"Today we're super focused on getting this thing launched," Janes said about the first Amazon Go Grocery location.

# data[].attributes.attachment_link.title
Amazon is opening its first full-size, cashierless grocery store. Here's a first look inside

# data[].attributes.attachment_link.description
SEATTLE - Amazon is cracking further into the $800 billion U.S. grocery industry, keeping the likes of Walmart, Kroger, 7-Eleven and Instacart on their toes. The online behemoth on Tuesday is opening its first, full-size, cashierless grocery store. Five years in the making, the Amazon Go Grocery is in the Capitol Hill neighborhood of Seattle, in the Amazon corporate headquarters' backyard.

# data[].attributes.body
This video game is about androids being used in the city of Detroit for labour and explores how these androids become self aware and rebellious.

https://www.pcgamer.com/detroit-become-human-review/

https://www.youtube.com/watch?v=8a-EObAhYrg&t=1s

# data[].attributes.body
Due to WMDs and the Us News Ranking list for schools, the more applications going out to schools lower the acceptance rates for almost every school.

# data[].attributes.attachment_link.title
Acceptance rates at top colleges are dropping, raising pressure on high school students | CNN

# data[].attributes.attachment_link.description
The college admissions cheating scandal shows just how far some people are willing to go to get their kids into an elite school. And with acceptance rates at top universities falling to record or near-record lows, teens are facing increasing pressure.

# data[].attributes.body
This will be really interesting to see develop--I'm sure it's not the last news we'll hear about Clearview AI.

# data[].attributes.attachment_link.readable_text
Clearview AI, the controversial company working with police departments across the US, says its client list was stolen by someone with "unauthorized access."

The company markets a facial recognition tool that lets police upload a photo of a face and search it against a database of billions of images scraped from social media to identify suspects.

A Clearview AI spokesperson confirmed to Business Insider that the breach, first reported by The Daily Beast, included the company's entire list of customers.

Visit Business Insider's homepage for more stories.

The full client list of Clearview AI, the embattled facial recognition company, was stolen by someone with "unauthorized access," The Daily Beast first reported.

The company has drawn backlash from privacy advocates and major social media platforms for its facial recognition tool, which lets police use a photo of a person to search a database of images from social media, identifying individuals based on their faces. The breach is notable because Clearview markets its services to law enforcement agencies and has previously avoided disclosing who its clients are.

Clearview AI attorney Tor Ekeland confirmed The Daily Beast's report in a statement to Business Insider. Ekeland said the breach didn't result from a hack into Clearview's servers, but rather a "flaw" that gave someon unauthorized access to the company's client list.

"Security is Clearview's top priority. Unfortunately, data breaches are part of life in the 21st century," Ekeland said, adding that the company will "continue to work to strengthen our security."

The New York Times reported in January that the company's customers include hundreds of law enforcement agencies across the US and Canada, including the FBI and Department of Homeland Security.

The company is also looking to expand its client base - a spokesperson told Business Insider in February that Clearview's service has "received inquiries from law enforcement agencies all over the world."

Facebook, Twitter, YouTube, and other companies have threatened legal action against Clearview AI, instructing the company to stop scraping public images from their platforms to build its faces database. Clearview founder and CEO Hoan Ton-That responded by arguing that gathering publicly-accessible photos is protected by the US Constitution's First Amendment.

Got a tip? Contact Aaron Holmes at (706) 347-1880 or at aholmes@businessinsider.com. Open DMs on Twitter at @aaronpholmes. You can also contact Business Insider securely via SecureDrop.

# data[].attributes.attachment_link.title
Clearview AI, the controversial facial recognition company partnering with police, says its entire customer list was stolen in a breach

# data[].attributes.attachment_link.description
Clearview AI, the controversial company working with police departments across the US, says its client list was stolen by someone with "unauthorized access." The company markets a facial recognition tool that lets police upload a photo of a face and search it against a database of billions of images scraped from social media to identify suspects.

# data[].attributes.headline
How Facebook targeting ads are more complex than they let on.

# data[].attributes.subject
How Facebook targeting ads are more complex than they let on.

# data[].attributes.body
This article explains how Facebook is able to gather bits of information about us and then use that data to make ads pop up on other sites we visit.

“Because the social network has so much data, it's possible to target hyper-specific audiences with extreme precision.”

While this can sometimes be good, it can also be a bad thing. In chapter 4 of WMD O’Neil talks about how for-profit colleges and other companies are targeting low income people through their information and appealing to their interests.

# data[].attributes.attachment_link.title
Facebook’s Targeted Ads Are More Complex Than It Lets On

# data[].attributes.attachment_link.description
The social network’s vice president of ads described a simple picture of how advertising works on the platform. But there’s nothing simple about giving advertisers a near-endless number of data points to target you with.

# data[].attributes.headline
How a guy used 99 phones in a cart to create traffic jams on Google Maps

# data[].attributes.subject
How a guy used 99 phones in a cart to create traffic jams on Google Maps

# data[].attributes.body
This experiment was carried out in a bid to make us think about the data that we rely on every day, and to consider the amount of space cars take up on our streets.

"For Google, it seems that the elaborate stunt may help it to tweak its service to detect antics like this in the future — perhaps carried out by someone with more malicious aims".

“Traffic data in Google Maps is refreshed continuously thanks to information from a variety of sources, including aggregated anonymized data from people who have location services turned on and contributions from the Google Maps community,” the tech giant said in a statement. “We’ve launched the ability to distinguish between cars and motorcycles in several countries including India, Indonesia, and Egypt, though we haven’t quite cracked traveling by wagon.”

It added: “We appreciate seeing creative uses of Google Maps like this as it helps us make Maps work better over time.”
https://www.digitaltrends.com/mobile/how-a-guy-used-99-smartphones-to-create-traffic-jams-on-google-maps/

# data[].attributes.body
This article discusses a type of dog-face recognition technology that is helping WSU officials in Africa know whether or not a dog has rabies and/or has been vaccinated for rabies.

“This technology means we don’t have to insert expensive microchips to identify each vaccinated dog,” explains Felix Lankester, director of WSU’s Rabies Free Tanzania program. “It saves us time and money, both very important commodities in our mission to eliminate human rabies in East Africa.”

https://news.wsu.edu/2020/02/10/facial-recognition-next-step-fight-rabies/

# data[].attributes.body
Fake job ads, mentioned in O'Neil ch. 4

# data[].attributes.attachment_link.readable_text
Neutron Interactive, a Salt Lake City-based business, regularly posted fake job listings on popular job websites including Monster.com and CareerBuilder.com for the purpose of tricking job-seekers into visiting its own websites, according to former Neutron employees. These Neutron Interactive websites, still operating today, also promise jobs, but in reality they usually offer none; their real purpose, say these former employees, is to convince visitors -- low-income people, single mothers, veterans, the elderly, all in desperate need of jobs -- to provide their cell phone numbers, so marketers at other companies, which pay Neutron for the leads, can sell them overpriced for-profit college programs.

Neutron Interactive's website highlights the company's membership and active participation in the for-profit colleges' main trade association, APSCU. Until last week, Neutron bragged on its site that "Some Of Our Biggest Fans" include Corinthian Colleges' Everest University, EDMC's Art Institutes, and Bridgepoint's Ashford University -- all major for-profit colleges. After I pointed out this boasting out in an article, it has apparently disappeared from the Neutron site, but as of today, Neutron is still listed by APSCU as one of its members.

With APSCU leading the effort to overturn the Obama administration's "gainful employment rule," and with the collapse of Corinthian raising issues about how to protect students when predatory for-profit colleges fail, it's crucial to understand that this industry signs up many of its students through blatant deceptions.

Such deceptive strategies have helped turn Neutron Interactive into a lucrative "lead generation" company and made its founders wealthy, according to the former employees. But Neutron had to regularly change its tactics, the employees say, as the legitimate job websites and other businesses recognized the scams and shut them down.

Recently I reported on how at least some of the visitors who entered their contact information on Neutron Interactive sites, and at similar bait-and-switch sites offering food stamps and Medicaid, were promptly contacted by workers in a call center operated by another business, called EdSoup, also located in Salt Lake. About 95 percent rejected the EdSoup pitches to discuss college programs, but the remaining 5 percent were passed on to another layer of call centers operated by or for for-profit colleges, including the University of Phoenix, Brown Mackie, Everest, Colorado Tech, IIT Tech, and Kaplan. EdSoup employees who worked at the company until earlier this year gave me the scoop on this shameless, deceptive operation, and backed it up with documents.

Within 90 minutes of my posting that story on Republic Report and The Huffington Post, the Neutron site I described, localemploymentnetwork.org, had been shut down. Perhaps a coincidence, or perhaps some concern about suggestions I made that the Neutron and EdSoup operations might be violating federal and state laws. The next day I posted again, describing other similar sites apparently run by Neutron. Those sites are still online at this writing.

Now, three former Neutron Interactive employees have come forward to tell me more about Neutron's piece of the shady world of for-profit college lead generation. Neutron's operations were "wrong -- illegal and immoral," says a former Neutron manager.

Two of the employees, the former manager and an early tech hire of the company, asked me to not use their names, citing concerns about retaliation. The third, Jason Gill, a former senior web developer at Neutron, was ready to be on the record. The accounts of the three employees were consistent -- and disturbing.

Neutron's founder and CEO Dan Caffee and its co-founder and partner Shaun Ritchie have not responded to my requests to discuss their business.

How does APSCU member Neutron Interactive operate and serve the for-profit college sector? The former Neutron employees laid it out for me.

The biggest question I had after my previous reports was: How do job-seekers end up at these scam websites in the first place? Google search has gotten smarter over the years, so that all the search engine optimization (SEO) techniques in the world can't normally draw search users to websites that deliver no actual content.

But Neutron had other schemes to pursue.

The former tech employee joined Neutron at its start in 2008. Neutron's founders had presented the firm as a marketing and advertising company, but, once hired, all the tech employee was asked to do was build landing pages -- the fake job sites, with stock photos of smiling nurses, police officers, and office workers. New sites all the time -- if one didn't perform well, it was quickly taken offline and another one built. The Neutron landing pages in fact offered no actual jobs. Eventually, this employee "got curious -- why are we doing this?" and started digging around. Soon it became clear: Neutron was trying to find visitors for the purpose of "suckering them in to applying for for-profit colleges."

To satisfy their curiosity, Neutron employees listened to call centers, operated by other companies, trying to sell college programs to Neutron leads, and, says the tech employee, "it was just sickening" -- vulnerable people facing a multi-stage process of deception. The call centers were "preying on people who were desperate" and pushing them to colleges that would make them "worse off than when they started" with "insane amounts of debt.... A guy said, I just lost my job, I haven't had a job for three years, I've looked everywhere. And the rep says, well, you can get a job, and there is opportunity -- playing off the guy's emotions -- for people like you with no other options, here are some college training programs you can attend."

Neutron sold some of its leads to call centers like EdSoup, whose telemarketers would screen the leads for for-profit colleges. Neutron employees said Caffee sometimes visited another company's call center in the Salt Lake area -- perhaps the EdSoup office.

EdSoup reps tell me they would hear from 19 out of 20 people called that they had no interest in college programs. The one in 20 EdSoup targets who was interested would then get called by the colleges directly.

But Neutron had a strong incentive to cut out middlemen like EdSoup and sell leads directly to colleges. The colleges were paying lead generators like Neutron $30 to $60 per lead. EdSoup was paying Neutron just $1 to $2 per lead. So Neutron was selling some leads directly to colleges including University of Phoenix, EDMC, Corinthian, Bridgepoint, Kaplan, ITT, and Daymar, according to the former Neutron manager.

But the former employees say the for-profit college industry was scrutinizing Neutron for the quality of its leads. So many of the prospective students whom the colleges reached through Neutron leads would say, why are you calling me? I was looking for a job, not college.

The former tech employee believes the client for-profit colleges knew that Neutron was engaged in improper tactics, but "the schools turned a blind eye." The employee added: "I think for-profit schools are just the devil, because they want to play off the deceptive tactics [of lead generators].... I didn't know that the University of Phoenix was a bad deal until I went to work at Neutron."

The tech employee eventually learned how Neutron was attracting people to its sites. Neutron had hired and fired a number of search engine optimization specialists, but SEO didn't succeed in getting the company's scam sites high in search engine rankings. They tried paid Google ad words, but that produced a poor return on investment.

What was working, was this: Posting listings for generic jobs -- like dental assistant -- on Monster.com, with a link to a Neutron fake jobs site. None of the jobs listed actually existed, and the Neutron site offered no links to jobs, or it just scraped unrelated job listings off legitimate job sites.
Getting caught, but getting rich
The fake ads leading to the fake sites became Neutron's "cash cow," according to the former tech employee. But it required Neutron to move the ads around.

Monster got wise to the scam, and didn't want its site polluted with fake listings, so it banished Neutron from its site.

Neutron started posting its fake job listings on another job site, Indeed.com, "but," says the former tech employee, "as soon as they figured out what we were doing, they kicked us out."

Neutron also was posting its fake ads on CareerBuilder, according to the former manager, until CareerBuilder, like Monster and Indeed, kicked Neutron off. Job seekers had been complaining to CareerBuilder about the fake Neutron listings.

Neutron had to keep "scrambling" to find job sites where it could post its fake ads before it got banished, according to the former manager. It was a constant cycle of getting caught and moving the scam down the road.

Caffee told the tech employee not to divulge to others in the industry how Neutron got its leads.

But Neutron did keep finding new places to post its fake ads, and the effort was thriving. Millions in revenues were coming in. The office actually had a giant ticker updating its revenues on an ongoing basis. Early on, the ticker lit up when the company reached a million dollars in a single month.

The profits seemed to make Neutron's founders rich. Caffee had a big house, a Jaguar, a Land Rover. He traveled on private jets, taking regular trips to Las Vegas. Caffee, according to the former employees, was frequently on vacation, and his Instagram page shows a love of travel and lavish leisure.

Neutron overcame employee misgivings with creature comforts. The former manager said that Caffee "wanted it to be a fun, cool place to work." The tech employee said, "Neutron was like [the movie] "Fight Club" -- you don't talk about Fight Club. They spoil you -- catered lunch very day, free snacks, everyone got an iPhone and an Ipad right away. We will spoil you, and you don't question."
Trying a new direction ends in mass firings
People who did question the company's approaches or ethics, the tech employee says, got fired, or quit. If they went along, they got promoted.

Eventually, the tech employee told Caffee that the situation was untenable: "I gave him an ultimatum" to change things, "or I quit." Caffee responded that he would try to make changes, but when that didn't happen the tech employee finally did quit.

In August 2011, Neutron had hired a new vice president of marketing who had worked in legitimate staffing companies. The new VP was able to hire about a dozen people, and was told, say the former employees, to develop plans to move Neutron in a more conventional direction.

One of those hired by the new VP was developer Jason Gill. He was told that his job would be to do leads work for job placement. But like the tech employee, Gill was directed to constantly build new landing pages that were more of the same -- fake job sites. Sometimes a site would be tried for a day, and if it failed to attract people, dropped right away, and another one built. Gill directed me to 20 more Neutron sites, with names like myjusticecareer.com, healthcareerchoices.com, drivercareers.com, and unitedcareerservices.com.

"These are all," Gill says, "part of the same bait-and-switch network with exactly the same strategy -- farm info to sell to for-profit schools." Some of these sites today still actively harvest the phone numbers of visitors, while others are abandoned shells. Neutron also maintains its own actual college portal site, collegeoverview.com, for people actually looking for college programs; of course, it steers visitors to for-profit colleges instead of more affordable and reputable options.

In this period, Neutron, flush with cash, acquired a company called Talent Base, which it apparently wanted to leverage as part of an effort to build a real job matching site. But the operation had no client employers to list jobs. And Neutron management, when challenged by the new marketing VP, refused to pay to post actual jobs on Neutron's own supposed job sites.

Eight months after Neutron hired the VP, according to Jason Gill and two other Neutron employees who spoke with me, Caffee directed the VP to fire almost all of his hires -- 12 of Neutron's approximately 45 employees -- on a single day. Caffee said that the business model that the VP had sought to deploy wasn't working. The VP sat in a room and fired employee after employee, handing each a red envelope with information about severance benefits. Then, after the firings were complete, at the end of the day, Caffee and Ritchie fired the VP.

"I was shocked" by the Neutron leaders' action to fire an executive right after making him fire almost his entire team, said one of the former Neutron employees. "I had never seen or heard of that" in the corporate world before.

Jason Gill was one of the few people hired by the VP who did not get purged that day. But he did resolve to resign, and he did so soon, after just six months with the company and, he says, "all their shady practices."

Today, Neutron has downsized -- fewer employees, a smaller office space. Caffee reportedly is focused on running another business, called JobDash -- "The Student Placement Solution." But many of the Neutron Interactive sites are still live, and still programmed to ask unemployed people to type in their contact information.
This article also appears on Republic Report.

# data[].attributes.attachment_link.title
For-Profit College Marketer Thrived By Posting Fake Ads To Popular Job Sites

# data[].attributes.attachment_link.description
Neutron Interactive, a Salt Lake City-based business, regularly posted fake job listings on popular job websites including Monster.com and CareerBuilder.com for the purpose of tricking job-seekers into visiting its own websites, according to former Neutron employees.

# data[].attributes.body
In almost everything we do in our day to day lives...there is algorithm behind it.

# data[].attributes.attachment_link.readable_text
The challenging part for the service is accurately fetching real-time information about traffic on the roads. It aggregates the data collected from Location Services on users' phones to determine how slow or fast people are moving on the roads. This provides a near real-time view into the traffic patterns around your area.

Search

You may need to research a topic or figure out a problem using a Google search. Google's PageRank algorithm searches an index of ~60 trillion webpages and returns the results to you in less than a second.

Personalization

You return home after work and decide to unwind in front of the television. The recommendations you see on Netflix and Amazon Prime Video are powered by algorithms that analyze the correlation between the programs you watched and ones that you may be interested in.

Online Dating

Online dating is now a multi-billion dollar industry due to the growth of sites like Match.com, OKCupid, and eHarmony. These sites pride themselves on their ability to connect users based on a compatibility rating that factors personality traits, values, and interests.

# data[].attributes.attachment_link.title
How Algorithms Shape Our World | Code by Amir

# data[].attributes.attachment_link.description
What's an algorithm, anyway? Software plays an increasingly important role in our lives. We benefit from the work of thousands of developers on a daily basis. An algorithm can be described as a sequence of steps for accomplishing a task. Algorithms process vast amounts of data to drive services in

# data[].attributes.body
I forgot to share this article during Tuesday's class --> this is a more recent(Sept. 2019)discussion of why the US News ranking system might not be accurate.

# data[].attributes.attachment_link.readable_text
Share to facebook

Share to twitter

Share to linkedin

U.S. News has released its 2020 Best Colleges rankings. Despite some revisions in how the rankings ... [+]
Getty

U.S. News & World Report just released its 2020 Best Colleges rankings, covering approximately 1,400 colleges and universities. For the ninth straight year, Princeton tops the National University list, followed, not unexpectedly, by Harvard in second. Columbia, MIT, and Yale tied for third. Williams College tops the list of National Liberal Arts Colleges, and UCLA is No. 1 among Top Public Schools. All the schools ranked in the top 20 National Universities list in 2020 were in the top 20 in 2019. This rankings inertia stems from both institutional continuity and a largely repetitive methodology.

Though U.S. News has revised its methodology over time and continues to tinker with the weights assigned to various measures, fundamental problems remain with the approach, which is based on 15 measures of "academic quality." It is too easy for schools to game the system and falsify data. Several of the U.S. News's measures have become proxies for institutional wealth, and their relevance to academic quality is questionable. Nearly 50% of the weighted indicators are problematic on at least one of these grounds. Here are the most glaring problems:

Alumni giving. The percentage of alumni who gave to their alma maters over the prior two years accounts for 5% of the ranking. U.S. News says that such giving indicates student satisfaction and continuing engagement with a school. But giving is a notoriously easy measure to falsify and bears little relevance to academic excellence. U.S. News dropped the University of Oklahoma and the University of California, Berkeley from the 2019 rankings for submitting false information about alumni donations. And just this month, evidence came to light that the University of Southern California regarded donations from applicants' families as an admissions factor.

Smaller class size, at 8%, is another dubious factor. There are two problems here. First, the evidence is equivocal at best that smaller classes produce better student learning yet U.S. News gives it more weight than first-year student retention or the graduation rates of Pell students. Second, would anyone be surprised to learn that colleges play a lot of games to jigger average class size, like capping initial class capacity only to allow more students to enroll later?

SAT and ACT scores as a measure of student quality are weighted at 7.75%. One would think U.S. News might want to forget about standardized tests as a factor, given numerous revelations about the degree to which they are subject to cheating, a problem made so painfully apparent in the recent college admissions scandal. But that objection misses the bigger point: colleges use the SAT and ACT to exclude applicants so they can score higher on "student excellence," turning the rejection of students into an institutional asset. And they are not above using various tricks - such as delaying the admission of low-scoring students to the spring semester so their test scores will not be included in the fall semester's submitted data - to make entering students' scores look better than they actually are.

Faculty salary gets a 7% weighting despite the fact that U.S. News cannot assure that the highest paid faculty are the ones actually teaching undergraduates or that well-paid faculty are more engaged or successful with students than those who are less well-compensated. As a proxy for simple institutional wealth, this one tops the list.

Academic reputation is valued at 20%. This is an entirely subjective measure at best and a bogus one at worst. U.S. News claims it measures reputation through a survey it sends to more than 4,000 college presidents, provosts, admission officers. (Thankfully, the survey of high school counselors used in the past as another measure of reputation was dropped this year.) Hundreds of colleges are included in the survey. How does any president or provost even pretend to know the academic quality of so many institutions, let alone make quantitative distinctions among them? No wonder some college presidents have admitted they delegate a staffer in their office to fill out the survey. Or worse, they confess that they downgrade the ratings of peer institutions to make their own college look better by comparison.

How much students and families rely on college rankings to make their college attendance decisions is uncertain, but clearly the ratings play some role. More clear, and perhaps more troubling, is how much effort and money colleges spend climbing for a higher rung on the prestige ladder. Lavish capital projects, lower academic expectations and lucrative scholarships for students without financial need are just three examples of expedient and often expensive strategies that colleges continue to use in an unworthy pursuit for higher rankings.

Follow me on Twitter.

I am president emeritus of Missouri State University. After earning my B.A. from Wheaton College (Illinois), I was awarded a Ph.D. in clinical psychology from the Unive...

Read More

# data[].attributes.attachment_link.title
U.S. News Releases Its Annual College Rankings. Here's What's Wrong With Them.

# data[].attributes.attachment_link.description
U.S, News & World Report released the 2020 edition of its Best Colleges today. Despite some changes to the methodology, the rankings still are subject to major problems, including excessive subjectivity, institutional gaming, and an emphasis on institutional wealth rather than student learning.

# data[].attributes.body
Also check out thiscatdoesnotexist.com

# data[].attributes.body
From Noel-Levitz, ForecastPlus evaluates the likeliness that a student will enroll, and determines how much outreach to provide to them.

# data[].attributes.headline
Cost Cutting Algorithms Are Making Your Job Search a Living Hell

# data[].attributes.subject
Cost Cutting Algorithms Are Making Your Job Search a Living Hell

# data[].attributes.body
This article discusses different types of automated job screening systems, "forcing jobseekers to learn new and absurd tricks to have their resumes seen by a human."

Especially interesting is a new algorithm that scans/scores video interviews: HireVue

# data[].attributes.attachment_link.readable_text
Jeffrey Johnson was on one continuous job search, more or less, for 12 years.

After the recession shuttered the textbook publisher where he was putting his bachelor's degree in history to use, Johnson worked office temp jobs and delivered packages, on an Uber-like gig basis, for Amazon and a medical testing company around his native Baltimore. He went back to school for a masters in screenwriting and then a law degree. Throughout, he scrolled through sites like Indeed and ZipRecruiter looking for better, more stable work-or just something to tide him over between semesters.

About two years ago, he started getting emails asking him to take online "assessments" for jobs after he applied. Some were tests of basic office skills, like spreadsheet use and typing. Others were for legal knowledge. Some were dippy personality tests. At first, Johnson was excited. "I thought it meant I'd gotten past a gatekeeper of some kind and was in the running," he said.

Then the tests came quicker and more frequently. One in four jobs had an assessment attached, he estimates. He got emails prompting him to take an online test seconds after he submitted an application, a sure sign no human had reviewed his résumé. Some were repeats of tests he'd already taken.

He found them demeaning. "You're kind of being a jackass by making me prove, repeatedly, that I can type when I have two writing-heavy advanced degrees," Johnson said, "and you are not willing to even have someone at your firm look at my résumé to see that."

Johnson also did phone interviews with an Alexa-like automated system. For one job, he was asked to make a one-sided video "interview" of himself answering a list of company-provided questions into a webcam for hiring managers to view at their convenience. Or maybe an algorithm would scan the video and give him a score based on vocal and facial cues, as more than 100 employers are now doing with software from companies like HireVue .

Until he started as a legal writer for FreeAdvice.com last month, Johnson, 36, said he was at potential employers' whims. "I can't imagine I'd move to the next round if I didn't do what they said," he told Motherboard.

Companies are increasingly using automated systems to select who gets ahead and who gets eliminated from pools of applicants. For jobseekers, this can mean a series of bizarre, time-consuming tasks demanded by companies who have not shown any meaningful consideration of them.

"Obviously, in our society time is money," said Ifeoma Ajunwa, an assistant professor of labor and employment law at Cornell University and author of The Quantified Worker. "So if you are asking [job applicants] to spend so much time on an application then you are transferring the labor your HR department would be doing to the applicant, so it becomes an ethical issue."

Applicant Tracking System (ATS) software has long been used by high-end firms searching for executives and HR managers at large corporations that receive thousands of applications. The systems are increasingly being adopted in various sectors of the labor market. ATS vendor iCIMS claims it revamped Footlocker's process for hiring sales associates, and JazzHR brags that it helps a regional Pittsburgh construction company hire all its staff, including interns. (JazzHR and iCIMS both declined to comment for this story.)

Beating the Bots

Maneuvering around algorithmic gatekeepers to reach an actual person with a say in hiring has become a crucial skill, even if the tasks involved feel duplicitous and absurd. ATS software can also enable a company to discriminate, possibly unwittingly, based on bias-informed data and culling of certain psychological traits.

Lynne Williams, a Philadelphia-area career advisor, holds a seminar called "Beating the Applicant Tracking System." Every time, she braces for a wave of anger from the audience. "I can feel their blood pressure rise when I tell them what they are doing wrong," she said.

Their most important task, she tells crowds of jobseekers, is to parrot keywords from job descriptions. The most basic elimination function of most ATS software is searching résumés and cover letters for keywords. Many systems can't-or don't bother to-distinguish synonyms, like "manager" and "supervisor," so she says to rewrite résumés with each application, mindlessly copying words from the job description. Countless online guides for "beating the bots" recommend the same.

People find this task frustrating and are indignant over its irrelevance to their fitness for the job, Williams said. Others fume about all the time spent carefully crafting applications that were probably never seen by a human.

Jack Wei, a director of product marketing for the job site SmartRecruiters, said that "the moment a candidate applies [for a posted job], a 'smart profile' scrapes résumé info into a digital portfolio by extracting keywords." The employer then sees an automatically generated score, from 1 to 5, of their apparent fitness for the job. The platform distinguishes synonyms and word variances when making this score, but the employer can search using any narrow phrase or word they choose, Wei said.

According to data from the job site Glassdoor, 250 people apply to the average corporate job. Many ATS vendors sell their products on the suggestion that hiring managers are overwhelmed by applicants. When almost half of Americans work low-wage jobs, a good job of any kind will generate a long line of hopefuls, despite the official government unemployment rate being at a 10-year low of 3.5 percent.

ATS technology encourages applicants to find ways to cut in line, said Anjunwa. She has heard stories of people inserting common keywords in small white font on their PDF résumés, visible only to bots, to sneak into the next tier of candidates.

Applicants can also use services to help them beat the algorithms, like the website Jobscan, which will scramble words from a job listing into their customers' résumés for $49.95 a month.

Such tricks don't show relevant job skills, but perhaps vital job-hunting ones, Anjunwa said. "People see that only people who are savvy get jobs," she said, "and the others get shut out."

How Often Does the Following Statement Describe You at Work?

The next round of the screening process is often an assessment test. Such tests have been around for decades, but ATS software has made it easy to deliver them automatically to an applicant's inbox.

Indeed, the world's most visited job site, has been pushing assessments in recent TV advertising. The company offers employers online tests for basic aptitudes, like attention to detail and memorization and recall; job-specific skills, like bookkeeping and first aid; and more abstract competencies, like critical thinking and problem solving.

Most take up to 30 minutes. They still piss off jobseekers.

"I've been finding a lot of online assessments that come with the job applications I file, wrote a poster on the jobs subreddit. Every time I see one of those, I immediately cringe. I HATE THEM."

"They're usually very long ... and most of the questions I just. Can't. Answer," they added. "For example, a question such as 'I prefer to work in team rather than alone' completely depends on the situation and the kind of job I'm doing."

Indeed declined an interview but told Motherboard in a statement that "Indeed's free Assessment tool is not a burden to job seekers, it helps job seekers demonstrate their full capabilities to prospective employers," and that the tests "help job seekers stand out based on their skills instead of their previous titles, employers, or their highest level of formal education."

Smaller companies also provide assessments, for a fee. Atlanta-based Berke offers both aptitude and personality tests. Neil Morelli, PhD, an organizational psychologist and vice president of product and assessment, said some of the applicant outrage can "come from older assessments that last an hour or two and they can feel clinical." He added, "These large battery assessments are being replaced by more aesthetically pleasing and enjoyable assessments."

Morelli admits "enjoyable" is relative in this context, but said a goal of his industry is to produce tests that feel game-like but still convey useful information about a candidate.

The Berke Marketing Free Personality Assessment is a 78-question sample test, where every question is the same: "How often does the following statement describe you at work?" Start it and then comes a flood of phrases and adjectives: "lively," "disciplined," "leader," "angry," "fearless," "puts others at ease," "soft hearted," etc. The test-taker picks one of four options: "almost always," "often," "sometimes," or "rarely." Morelli said some of the terms in a test are virtual synonyms to suss out applicants trying to game it. Obviously, one would hesitate to tell a potential employer they are "often" "angry" at work and "rarely" "put others at ease," even if true.

The assessment tests applicants on psychology's "big five" personality traits of extroversion, agreeableness, openness, conscientiousness and neuroticism. Some managers consider these traits for even minimum-wage work.

Why Robots Don't Think Women Can Handle a Job at Amazon

In 2012, Kyle Behm took a break from university to deal with manic depressive disorder. He applied online for a low-wage job at a Kroger grocery store near Atlanta. A friend who worked there told Behm he didn't get hired because of the results of a personality test. His father, an attorney, filed suit against Kroger and five other companies that tasked Behm with big-five personality tests for a low-paying job, alleging they illegally screened for mental illness. Sadly, Behm ended his life last year before the case was adjudicated.

The Americans with Disabilities Act prohibits "employment tests that screen out or tend to screen out an individual with a disability or a class of individuals with disabilities" unless necessary for the job.

Morelli said that Berke's personality assessment is ADA compliant because it "does not meet the criteria for being a medical examination" and "is not invasive or used to infer mental health." But asking a candidate if they are "fearless" or "comfortable with others" may produce results indicating a condition like depression or social anxiety disorder.

Some advanced ATS have "learned" bias by incorporating variables that favored people who are already advantaged. Amazon abandoned its development of an AI-based hiring process when the predictive models favored male candidates. The system was relying on résumés submitted to the company over ten years, and because of the prevalence of men in tech jobs, the system began to downgrade résumés that included all-women's colleges or female-indicating phrases like "women's chess club."

The makers of more advanced applicant tracking systems are acutely aware of the bias problem, but are not certain of a solution.

Arya is a "recruiting platform" that uses predictive analytics to identify and classify job candidates. Madhu Modugu, the CEO and founder of its parent company, feels assessment tests are a poor indicator of job performance, and claims that Arya's platform avoids placing a heavy time burden on applicants.

"[Arya] would look at my history and my past, what kind of organizations and what kind of cultures you are exposed to," said Modugu. The system would then match the candidate against a composite profile of employees "who are the high performers in the culture of the company." If a company can't provide that information, Arya offers its own psycho-metric profile of who does well in the jobs that its customers are looking to fill.

The term "culture" can have some problematic connotations when applied to who is suited for a job. Modugu insists Arya measures the work culture of past employers compared to that of the prospective employer.

But he said he is aware of problems like the one that plagued Amazon: If the system is fed data that shows white males have been the "best" employees-because prejudice allowed them into those roles more easily-it will conclude that white males are the "best" candidates. "The AI has interpreted the data correctly," said Modogu, "but it has generated biased results because the information is biased."

The solution is not simple, he said. Developers need to work out systems that can better fight bias and HR managers need to take active steps to increase recruitment from non-traditional pools.

On top of issue of discrimination, the emphasis on data in recruiting can make people feel stuck in a role. Data points come from past experience, and neglect factors like ideas, ambition and understanding of an industry.

Nick Thorch once worked in Microsoft's inside sales division, selling the company's products to other large businesses. He wanted to transition to product management. After spending time fielding customer complaints about Windows Vista, he felt he had insight into how software should be developed. He applied for thousands of product manager jobs in Silicon Valley.

"The only time a recruiter was interested in me was for another inside sales position, even if my cover letter, résumé and career objective statement strongly supported a range of business roles," Thorch said. "ATS mentality keeps people pigeonholed in their past, rather than what they feel inspired to do."

SmartRecruiter's Wei saidthat the effect of ATS on narrowing career pathways presents a "good question" for the industry.

"On a technological level alone, there is only so much alone you can do," he said. Recruiters need to identify people who might be viable for a career change. "You lead with people and process first, but with technology alone you can't have lasting change."

Asking Some Hard Questions

When it debuted in 2013, HireVue's AI analysis of video interviews seemed like an endgame for job application automation. A candidate answers questions to a silent webcam and uploads a video. A program then scans their facial features, word choices and vocal indicators to determine-through some murky, trademarked science-if they should advance to the next round. The Utah-based company once had $93 million in venture capital and more than 600 clients, including Goldman Sachs and Hilton.

Last year, artificial intelligence scientists called HireVue's methods "pseudoscience" and "profoundly disturbing" in a Washington Post article, and the Electronic Privacy Information Center filed a complaint with the Federal Trade Commission alleging it violates jobseekers' privacy through facial recognition.

Cornell's Ajunwa said AI analysis of mannerisms presents some of the worst potential for discrimination in the labor market. The process would be a roadblock for people on the autism spectrum and for many from foreign countries, as acceptability would doubtlessly hinge on neurotypical, American mannerisms. "What if your culture says not smiling is respectful?" she asked. "There are many cultures where people do not laugh and smile like Americans do."

HireVue did not respond to a request for comment. Its website no longer touts AI analysis of videos as a service. But it is one of several companies that gather self-made recordings from job applicants answering interview questions. The applicants upload them for a manager to, perhaps, view at their convenience in another new task in the automated application process. VidCruiter and Modern Hire, two other prominent video interview companies, also declined Motherboard's requests for an interview.

"It's just your typical interview questions like, 'What's a time when you failed at work?'" said Jeffrey Johnson. He recalls a herky-jerky system that would beep to prompt him to stop mid-answer.

He's not sure if an AI or a person looked at his video. He submitted it on a Friday and was rejected for the job the next Sunday.

VidCruiter's website describes the appeal of having a limitless library of candidate interviews, on tap. In a video, a "senior recruiter, " likely an actress, complains of "spending eight hours doing phone interviews" and "pulling [her] hair out" over the lousy candidates. She then describes "a system that will allow you to ask an unlimited number of candidates as many questions as you want."

"I'm doing something else while the system is interviewing my candidates," she says with a smile.

The message is clear: She's offloaded much of her work to someone else.

Ajunwa said automated systems will probably continue to amass between jobs and jobseekers. "I think that's the way it's going to advance," she said. "Companies have come to count on it." She has called for mandated auditing of algorithmic systems to ensure against "bias in, bias out" preferences like the ones that affected Amazon.

Should job applicants rebel? Should they refuse to take online assessments or to upload video faux interviews or engage the next faceless gatekeeper?

She encourages candidates to take a principled stand if they are in a position to do so, if they are already employed or have good prospects. Everyone should monitor tests that "echo mental health" or show other signs of bias, she said. But she doesn't know where exactly to draw the line to refuse to comply with the process.

"That's a tough question," she said, "because if you need the job, you need the job."

# data[].attributes.attachment_link.title
Cost Cutting Algorithms Are Making Your Job Search a Living Hell

# data[].attributes.attachment_link.description
Jeffrey Johnson was on one continuous job search, more or less, for 12 years. After the recession shuttered the textbook publisher where he was putting his bachelor's degree in history to use, Johnson worked office temp jobs and delivered packages, on an Uber-like gig basis, for Amazon and a medical testing company around his native Baltimore.

# data[].attributes.body
Aya Glaser

New York City cops are in a fight against their own police department. They say it’s under the control of a broken computer system that punishes cops who refuse to engage in racist, corrupt policing. The story of their fight, and the story of the grouchy idealist who originally built the machine they’re fighting.

This is an good example of an algorithm that becomes racist and oppressive over time. The data fed into it perpetuates higher quotas, because the machine determines that certain areas have more crime, but the machine itself is creating those inflated numbers.

# data[].attributes.attachment_link.readable_text
PJ VOGT: Hey, quick warning before we start the show. This week's story has a couple vivid descriptions of sexual violence. They're both in the second part of the story, but if that's not something you want to hear, this might be a good one to skip. Ok.

[REPLY ALL THEME MUSIC]

FROM GIMLET, THIS IS REPLY ALL, IM PJ VOGT

PJ: At 33 years old, after years of working in a meat market, Pedro Serrano switched careers. He'll never forget his first day at the new job.
PJ: Where did-where'd you get sent? ￼PEDRO SERRANO: Uh, the 4-0 precinct. That's, uh, 138th Street and Alexander in the Bronx. So now we go there in the 4-0 precinct, eyes are wide. I'm a 30, I dunno, 33 years old, old man. But I feel like I'm a rookie. This is brand-new. If you ever change jobs and feel funny that first month etcetera, that times 100- PEDRO: Because you have a gun and radios. They sent me to a very active area, in regards to crime. A lot of crime. Everywhere. PJ: What kind of crime? PEDRO: You name it. I mean it's everything. There's robberies, uh, there's rapes, there's larcenies, there's assaults, you name it. I mean, whatever's out there, it's out there.
PJ: The thing that was weird about the job was that Pedro's bosses didn't really seem to want him to pursue the actual, violent, serious crimes that he saw. What they wanted him to do instead was just to write summonses. Summonses are just the tickets that cops give out for the low-level stuff, misdemeanors. You'd give a summons to a guy drinking a beer in the street or riding his bike on the sidewalk. There was a lot of pressure to write summonses.
PEDRO: One of the ones that, you know, never leave my head was it was overtime. We're ordered to write five. And the van- PJ: Write five like you had to-you have overtime but you gotta come back with five summons? PEDRO: Five summonses or get dealt with. I'll explain that later. Well, we, uh, we're driving around and the senior guy stops and says, "All right. This is one."
PJ: It's really early in the morning. The streets are almost completely empty. It's hard to even find a person, let alone somebody doing something wrong. Their boss is pointing at this man who's just standing on the sidewalk alone, outside a store. But the van stops.
PEDRO: Some guy jumps out. And this one guy, in front of a bodega, doing absolutely nothing, they gave him a summons for blocking pedestrian traffic. You know, we were just shaking our heads like, "What did you give him?" "Blocking pedestrian traffic." And they just start laughing. I'm like, oh wow. All right. So we move on.
PJ: Pedro says the next stop was this Mexican man who was just sitting alone on a stoop. They wrote him up for the exact same thing: Blocking pedestrian traffic.
PEDRO: And this was all night until all of us-there was like a four or five of us in the van, until everyone had five.
PJ: Pedro was so confused by what had had happened that night, he actually went home, and looked up the definition of blocking pedestrian traffic. These guys had not been blocking pedestrian traffic. This was absurd. And Pedro didn't know it, but all over the city cops were getting pushed in the exact same way -- to aggressively write summonses to people for doing seemingly nothing. I talked to another cop, this guy in Brooklyn named Edwin Raymond.
EDWIN RAYMOND: After the academy, I would run into officers that I was in the academy with. And it'd be like, "Oh, hey, what's up? Are you still at transit?" And the third question, without fail, the third question was always, "What do they want from you guys over there?" That's how much this is part of the culture.
PJ: Week to week, the summonses Edwin was being told to write could change. One week, they might want turnstile jumpers, another week they'd want open containers. And they'd tell him, the reason we're having you do this is because when you summons people, it's an opportunity to check if they have a warrant. And if they do, you can arrest them, because it means they've committed a serious crime. It made sense to him.
EDWIN: You see, at first I felt good because when someone would have a warrant it felt good. It was like, okay he had a warrant. Until one day, it was just, this- the-girl looked like she was 10. And she wasn't my arrest. I arrested someone else with something but she was in the cells. And I said, "Young girl, like, what are you doing here?" She's like, "Oh, like, um, me and my friends, we went through the turnstile together." I said, "Where's your friend?" "Oh, my friend, she got a summons and they arrested me." I said, "For what-how old are you?" "16." I said, "But for what? Just going through the turnstile with your friends?" Like, "Yeah."
To Edwin, it didn't make sense that this girl was in jail just for sneaking through a subway turnstile. He asked her, are you sure you're not leaving something out?
EDWIN: "Did you give the officer a hard time?" "No." So, that's when I said, you know, aft-I saw who her arresting officer was. I said, "Yo, what'd, what'd you get her for?" "Oh she, um, she had a warrant." I said, "A warrant?" I said, "For what?" He's like, "I don't know. Like, I don't care. She has a warrant, I got my collar. You know, I'm meeting my quota."
So Edwin goes back to the girl still just trying to figure out what happened.
EDWIN: So I said, um, "Did you ever receive a summons?" She was like, "What's that? I said a ticket?" She was like, "Yeah." "When?" "On my birthday." On her 16th birthday. It was a few months after. I said, "What happened?" She was like, "I went to the movies with my friend. We walked out of the movie, and the officer said, 'Didn't I tell you guys get away from the corner?' And just gave all of his tickets." And as a 16-year-old, the last thing she's thinking about is going to court to handle the ticket.
So what had happened was she ignored the summons, and the summons had turned into a bench warrant. And once you get a bench warrant, if a cop stopped you, they put you in jail. Which made Edwin feel like, wait a minute, like, the whole point of these stops is we're supposed to find bad guys with warrants, not just people who ignored their summonses. Like, what is going on here?
EDWIN: That's when I started saying, I got to look into these warrants. So now when I was stopping folks, running their names and doing a little bit of research into the warrants, I said, "What the fuck is this?" It was summonses from my colleagues outside of the transit system who were just trying to meet their own quotas. So it just-it was like self-perpetuating.
PJ: Edwin and Pedro didn't know each other. But they were having the same problem, and they were asking their bosses the same question -- what is going on here? Why can't we just do just normal, honest policing? And the answer was always the same -- the reason all this is happening is because of CompStat. This computer program that the NYPD uses to measure every single cop and tell them in excruciating detail how to do their job.

Their bosses didn't like CompStat. They'd tell them, "I know it's not fair, but I have to do what CompStat says. Because if I don't, they'll just punish me. So do what you're supposed to do and let me try to protect us."

The thing they couldn't figure out was why the system existed in the first place. And if all their bosses hated it, who liked it? Who wanted things to be the way that they were?

So a year ago, I started trying to answer this question. And it turns out that compstat really just comes from the brain of this one person-a person who was not at all the person I was imagining. He was this idealistic, grouchy, smart weirdo, with a very complicated brain. His name was Jack Maple. And the machine that he first designed 25 years ago was so different from the machine that Pedro and Edwin would encounter. Jack's machine, was supposed to do nothing less than save the city of New York. And before things got bad, it did.
JACK MAPLE: How is that sounding? CHRIS MITCHELL: Um... JACK: Do you want to go down some of these things and I just talk? JACK: --this is your area. JACK: You know what I mean? I'm the guy that puts the fucking pins on the maps.
PJ: A few years before he died, he sat down with this journalist named Chris Mitchell, who he wanted to tell his life story to.
JACK: You really think I'm crazy, don't you. CHRIS: (laughs) JACK: You do. You want me to- CHRIS: You're a little surprised about what? JACK: I don't know what I want Chris you're the writer. I'm just someone that has nothing to do and I write notes of madness.
PJ: There's over 20 hours of these tapes. And since I found them last year, I've listened to them over and over again.
[JACK TAPE IN THE BACKGROUND]
PJ: And I've also talked to a lot of the people who were closest to Jack. Trying to understand what it was he thought he was building.
[MUSIC POST OUT] BILL COURTNEY: So I have the real story, how it started. So I'll tell you the real story. PHIA: I want this closer for the real story. [Mic noise]
PJ: This is Bill Courtney, he's a retired cop.

BILL: All right, so I, uh, I became a transit cop in 1983. You, you just can't understand how bad it was back then. I don't know like when you were born, but-
BILL: Okay. When you were five years old, New York City was a horror show. NEWSCLIP: This is WYNN. And the murder rate just keeps on rising. BILL: It was just one horrific event after the next. NEWSCLIP: That's an average of five or six homicides a day. BILL: As a police officer with a gun, I felt like I was a victim, just living in this city. Get up in the morning, and, as you got close to getting to work, your heart would start pumping. You'd start thinking like, what, what's going to happen tonight? [siren noise] There was a lawlessness out on the streets. Things just unfold in front of you as you turn a corner and walked right into a robbery or walked into a guy holding somebody at gunpoint or a stabbing. And it was right there. COP RADIO: Report of a male shot in the head. BILL: It was just, like, mayhem. COP RADIO: We've got a homicide in Brooklyn [siren noise]... BILL: Non-stop. People screaming and yelling. Someone coming running around the corner and, you know, a woman holding the strap of her purse, which is now gone and a bruise on her eye. Just non-stop craziness. People see you, stop in their tracks, take off running. You take off running after them, and you don't even know why. And you come home and you lock yourself in your apartment and you pray that nothing bad happens to you or your kids. It really was a horrible place to live.
PJ: All day, every day, Bill showed up to work and just felt the overwhelming feeling that for all the work he did, none of it was really going to make a difference.
BILL: We would arrest people for not paying their fares. We'd give out numerous summonses for everything from urinating to disorderly conduct. It meant nothing. And nobody knew what to do to make it better. [MUSIC - FLUTE IN]
PJ: The only person with any optimism about the situation was Jack Maple.

Jack was a sergeant in charge of Billy's squad. And Jack was convinced that everything wrong with New York City could be fixed if only they would put Jack Maple in charge of it. Jack's bosses had a different belief - they believed that Jack was delusional. The guy walked around like he thought he was a character from a 1930s detective movie.
BILL: People were uh-didn't like the way he dressed, didn't like the way he stood up to them- PJ: How did he dress? BILL: So Jack wore like spats, a bowler hat, a sport coat with baggy pants and shades, and he looked like Edward G. Robinson meets Truman Capote or something, I don't know.
PJ: In the New York City Police Department in the 1980s, Jack was like the equivalent of that kid who just wears a suit to school everyday for no reason. And what made this even more ridiculous was that Jack was a transit cop. Transit was its own, like, dinky department. Basically they were seen as like glorified mall cops. But Jack acted like being a transit cop was the most glamorous job in the world.

After work, he would go to all these extremely high-end, fancy bars. Places where celebrities went, like the Oak Room at the Plaza Hotel.
BILL: You know, here's Jack with his homburg and his spats, going out after work, amongst these rich people that Jack used to call faffingtons, you know. And everybody would always ask, "Who is that guy?" He'd go to the bathroom, and they'd say, "Hey, who is that guy?" They thought he was an actor, a producer, whatever. And then, if people would come up to him and say, "Who are you?" He would always, he, his response quite often was, "My name is Waldo Wedemeyer, (PJ laughs) and I get shot out of the cannon at Ringling Brothers Circus." (PJ laughs) How he gravitated towards police work, I really don't know but um, he really wanted to be famous. He really wanted to, to make his mark on the world. [MUSIC]
PJ: Meanwhile, at work, Jack was always on the verge of getting fired. The thing was, he kept ignoring the fundamental rule of being a transit cop, which was just stay in the subways. In his mind, he was like, I'm a crime-fighter. Wherever there is crime, I must go. And so he was constantly leaving the subway. In the tapes I have of Jack, he tells Chris his favorite place to go when he would leave. Times Square.
[Times Square people hawking stuff] JACK: 42nd Street at that time, the hookers are four deep. You know, there were still the pimps with those Eldorado Cadillacs, like with the hearts for windows. CHRIS: Uh huh. JACK: 40th street is real dark around there. At 8th avenue there's a bar over there, on 41st and 8th called The Terminal Bar. Appropriately named. CHRIS: Uh huh.
PJ: So even though Jack wasn't supposed to leave the subway, he'd found this loophole that was letting him do it, which was that there was one hour a day-his lunch break-where technically he could go wherever he wanted.
JACK: I'm playing by the rules, but the great thing about, for that hour a day, you know, I was policeman to the world.
PJ: Jack said that he was getting an education in Times Square. He said he was getting a master's degree in crookology. He was starting to believe that he could read crime the way, like, a weatherman can read a weather pattern. I talked to this guy Jimmy Nuciforo who worked for Jack. He told me a story about Jack doing this.
JIMMY: He would say, "Tomorrow, Jimmy, we're working, you and me." I'm like, "Jack, it's Thanksgiving." He says, "Yeah." He says, "Plenty of pickpockets out there." He says, "We're workin'!" So and what we would do is, on 30th-and he taught me this-on 34th Street in Manhattan. He said, "It's the easternmost exit in Macy's on 34th Street, there's a stairway that goes down into the subway. And so, during the holidays everybody's coming out of Macy's and going down into that Subway." And he said, "It creates a bottleneck. So all these people get stuck at the top of the stairs," so he said that is the perfect situation for pickpockets. And he was absolutely right. So we would play that 50-foot area there, and every day we would lock up pickpockets there. You know, and they were like all over these people. Um, you know, like bees on honey.
PJ: Stuff like this was why Jimmy and Billy thought that Jack was a genius. But from their boss' perspective it was like, it's not your job to arrest people outside of the subways. Don't arrest people in California. Don't arrest people in New Jersey. Don't arrest people in Times Square. You are a transit cop.
BRIDGID O'CONNOR: So they transferred him to the Bronx. So, he was living in Howard Beach and to take the train all the way to the Bronx, that's like a punishment. You know, it could take you two hours to get to work on the train.
PJ: This is is Bridgid O'Connor. She was a transit cop, later on, she and Jack actually got married. She said that when the department decided to punish Jack in this way, they made a crucial mistake, which is that they forgot that to get from Howard Beach to the Bronx you had to transfer through Times Square.
BRIDGID: So when he got off at Times Square, he always made an arrest (laughs). PJ: (laughs) And then once you make the arrest, then what happens? BRIDGID: Then you have to process it, and then you're down in central booking for 12 hours. You know, it was like, that's a whole day. Once you make the arrest, you're done for the day. And he never made it to the Bronx. He never made it, ever. He was assigned to the Bronx, and he made an arrest every day.
PJ: Over and over again, Jack would get transferred to some punishment post, some desolate street corner in the middle of nowhere, where they'd tell him, "Just stand there and don't do anything." And over and over again he would ignore them, and make an arrest. And eventually, they'd give up. They'd just chuck him back to transit. Transit was its own punishment. The Siberia under Siberia, Jack called it once. He spent over a decade down there, stuck in the tunnels.

For the people who believed in him, the tragedy of Jack Maple was that he was somebody who could have done something. He'd been this like bright young cop. He was the youngest detective in transit. And then it was just like, he couldn't help himself, he had to pick fights with the bosses. He had to spend every single night going to these fancy bars-he took out nearly $30,000 in loans against his house just so he could keep going to the Oak Room. It didn't make sense.

I talked to this guy Mike Daly. Mike was Jack's best friend. He's a reporter. And he told me the reason for all this was this thing that happened to Jack this one night when Jack was 22. The night he almost died.
MIKE DALY: He was at 42nd Street and Bryant Park in a canary yellow jumpsuit with Elton John sunglasses, buying drugs and locking up drug dealers. And he ended up wrestling for his own gun with this guy.
PJ: The guy got a hold of the gun and shot twice twice at Jack's head at close range. He barely missed. They ended up wrestling for it. It went off. And the guy got shot. Both of them survived. And Jack made the arrest.
MIKE: And at the end of the night, he's walking across Central Park South, covered with this guy's blood, with a muzzle flash burn on each cheek and he walks past the window to the Oak Bar. And he looks inside and there's all these (laughs) well-to-do wealthy people on the other side of the glass, drinking and laughing and it looked like a world where nothing bad could ever happen.
PJ: Jack knew if he had died that night, he would've just been a dead transit cop. And for the rest of his life, that left him with a lot of sympathy for other people who ended up on their backs, dying unimportant deaths.
MIKE: And he also saw what the city can offer you and what the city should be, and how great the city should be. And why shouldn't it be great for other people too? And why shouldn't other people live like they were on another side of a glass where nothing bad happened to them?
PJ: In 1988, after a decade in purgatory, Jack caught a tiny break. He got put in charge of this small squad that would investigate repeat offender robberies. The people who were committing tons of robberies in the subway. And Jack decided his first target as head of the squad would be this incredibly tricky problem that nobody had been able to solve, this thing that cops called "wolfpack robberies."
[MUSIC]
So here's what was happening. People who rode the subways were getting attacked. They were getting attacked by big groups of people, a minimum of five, but sometimes as much as like 20 or 30, who would just mob their victims on the subway. They'd run in and with either just their fists, or sometimes with knives or box cutters, they would just pummel the person, to just take everything they had.
NEWS CLIP: Some citizens are carrying weapons to protect themselves just in case there's trouble NEWS CLIP: It's a terrible situation, I--I-- .
PJ: Things were so bad that the public school chancellor had asked if the police could have special armed subway cars to escort kids to school. The 4,0000 members of the transit police had been unable to stop this. Jack was going to try to do fix it with 24 cops.
BILL: So Jack came up with the idea of starting a decoy unit. And so we all dressed like, you know, average everyday people that either get mugged or people that are on the train.
Everybody said these attacks were random, but to Jack they didn't look random, because he saw these patterns in who got attacked. Like for instance, Asian people were getting attacked way more than anybody else. He did a victimology study, he found that they were four times more likely to get attacked.

PJ: The NYPD had actually done decoy units before. But Jack did not like the way they did them. They were borderline entrapment. They'd had, like, an undercover cop on the train with a 20 dollar bill hanging out of their pocket. Then they'd arrest the poor guy who tried to take it.
BILLY: He said, you're - you know what-he goes, "You, you've got this boyish look to you."
But Jack's goal was that he wanted to target the repeat, violent offenders-the people who were intentionally targeting the victims who showed up in Jack's studies. So he said, our decoys should just be like an Asian cop who pretends to be asleep with a nice watching on. We're going to actually have cops dress up as every kind of victim that we see show up. So one day he looks at Billy.
BILLY: So I was like, well that's not gonna happen.
PJ: He says, "Billy, we're gonna put you in a dress."
BILLY: And we'll make my partner-one of my partners, Wayne Richardson, he'll be like a nerd with coke bottle glasses. And you'll, you'll walk together or sit together and everyone's going to just rob you like, like it's, you know, going out of style. So I come in Friday night and they have a wig, a bra, and some other things waiting for me.
PJ: Billy was this straight, macho cop, He did not want to do this. But that was Jack's point. He told him, they're never going to think you're a cop. They'll figure there's no way you'd agree to do it.
JIMMY: And then, you know, we would have to get more creative on that train to make people believe that we weren't cops. So we would do things like, the backups, we would roll joints right on the train out of, um, Lipton tea. We would use Lipton tea and roll joints. Uh, we would smoke those joints in the last car of the train, where you're not supposed to smoke, um, but just to make ourselves more believable. We would sell those joints, you know, on the train, back and forth to each other (PJ laughs), that type of thing, just to make people believe that these guys ain't no freakin' cops. PJ: Was there anything else that you would do just to like seem more credible? JIMMY: Yeah. We used to have a Boombox, we would blast music in there, and, uh- JIMMY: --it wasn't Frank Sinatra. And some of the-- the, uh--the backups would start dancing in that rear car and other guys. Perps would get up and they'd start dancing. And it's like a party and that's what it was known as, the party car.
PJ: So they stick Billy in middle of the subway car, and then around him there are a bunch of undercover cops who are dressed to look like random subway passengers. Jack would wear this really ugly Playboy Bunny sweater. Jimmy would dress up like, kind of like his idea of what like a punk rocker looked like.
BILLY: We were instantly robbed. Everywhere we went, you know, in, in a matter of minutes, we had-I think we had been robbed like six times in, you know, like an hour or something like that.
PJ: So, the subway doors would open, a group of perpetrators would walk on, looking for somebody to rob, and they'd see Billy, just looking like an easy victim. Somebody would pull out a knife to cut the necklace off his throat. It really never took much time.

PJ: But before they could get away, this crew of undercover cops jumped on them and arrested them all. It worked. Over and over again it worked. Not just because they were able to lock up a bunch of criminals, but because the tactic itself was so flashy and attention-grabbing, that it actually sort of advertised to people who would have committed these crimes that it wasn't safe. Somebody who looked like a victim could actually just be another undercover cop.

Wolfpack robberies plummeted from 1,200 a year down to 12. Andit was in the department, it was like for the first time, some of Jack's bosses seemed to think that maybe, he might be as smart as he seemed to think he was.
BILLY: Jack told me, "Listen," he goes, "I want you to cover this entire office with a piece of paper. I don't know what you gotta do, but I want every single subway station represented in this office."
So, credibility in hand, Jack unveils his actual big idea. The thing that will transform not only New York City, but the entire world. [MUSIC FADES UP] When he tells Billy what he wants him to do, it just sounds like an arts and craft project.
BILLY: So I was like, OK, OK. So I kept blowing Jack off. And I just thought it was an ominous task to actually have to get up on a chair and, and tape pieces of paper to a wall, representing every single subway station.
PJ: The New York City subway had 430 subway stations. Jack was telling Billy, "Go draw them all for me." It was a huge pain in the ass.
BILLY: Pages and pages of information, you know, on this dot matrix ancient printer from, you know, it seems like 1,000 years ago. And when you look it, it just looks like garbage.
PJ: But he does it. And when he's done, it actually looks crazy. It's like, they have this tiny office, and the walls are covered. It's 55 feet of paper. And now Jack shows up, just holding the crime stats for the subway. Like, a huge print out that has every single recent robbery.
JIMMY: And initially we would use markers or pens and color it in. And then we just started using, um, these different color sticky dots. And we'd put the dot on for the time of day that it happened and then we'd write the code right in the middle of the dot, which made it even easier.
PJ: So Jack tells Bill, "What we're going to do today is we're going to create the Charts of the Future. Here's what I want you to do. Take this like endless spreadsheet of all these different crimes, and start putting them on the map. So like here's the first one, uh, 4 a.m., somebody got mugged with a knife at Times Square. That'll be like a pink dot, put a pink dot at Times Square." There was another mugging at Chambers Street, put a pink dot at Chambers Street.

PJ: They'd seen crime maps before, and every map they'd ever seen had shown them exactly what they already knew. Once a month, you stick a bunch of pins on a map of the neighborhood, and you see that there's tons of crime. And it's completely overwhelming.
JIMMY: The color coding was huge. Because that really just spelled it out for you. It's just staring at you there, telling ya: Look! There's a problem over here. And it could either be one specific location or it could be one train line, ya know, the whole line and you see it going down. And they're hitting here one night, they're hittin' here, they're hittin' here.
But when they all stepped back and looked at what they'd made, when they looked at the Charts of the Future, they saw this map was different. This map, it showed you the subways the way Jack's brain saw the subways. It was updated every single day with every single crime represented by time. So, a bunch of blue dots at Times Square, those were all pickpockets. And in the afternoon, they'd be orange. Orange dots that were now down at West 4th Street, because they'd moved downtown.

PJ: One of the things they'd been wondering about was this huge uptick in purse snatchings-100 more purse snatchings than they'd had that month the year before year. And these purse snatchings were happening in a way more dangerous way. People were hiding between the trains and grabbing women's purses as the train sped through the platform. They were worried somebody was going to get killed.
JACK: I go to his apartment, so I knock down the door, and this motherfucker, I mean I gotta hand it to him. He jumps from the fire escape-he's living on the fifth floor-not the next floor down, he jumps the next apartment over and down one floor. You understand the jump I'm talking about now? So now, it's embarrassing, this motherfucker gets away, right?
And when they looked at the charts, they realized, oh, wait a minute, all these new purse snatchings are actually happening on one train line. Not only that, they just happen across a few train stations and always in the afternoon. So Jack puts all his cops on that one train and they're able to identify a suspect.
NEWS CLIP 1: An unprecedented drop in subway crime. Thousands of crimes prevented. Robberies cut three times faster than the citywide robbery rate.
PJ: So the guy gets away. But not long after that, Jack's able to catch him. And in the weeks after that, they see that this whole, enormous purse-snatching crime wave, it goes away. It was all just the one guy, which for Jack, confirmed this theory that he'd had, which was that crime was like any other industry-a small percentage of people did most of the work. And with the charts, he felt like he could identify the criminal overachievers, arrest them and make the city much safer.
NEWS CLIP 2: Go down into the subway! Subway riders in New York actually letting passengers off, being obedient...
PJ: Felony crime in the subway dropped 30 percent in two years.
[MUSIC]
PJ: New York City was as dangerous as it had ever been, but the subways were this, like, oasis of relative calm.

PJ: So in 1993, this new police commissioner takes over the department, Bill Bratton. And he says two things that are extremely shocking. One, he's going to cut crime in the city in half. And two, his second-in-command, the guy who's going to pull this off, is Jack Maple.
[MUSIC]
After the break, Jack Maple versus the New York City Police Department.
MIKE: The city police, they go, "There's this transit cop comes in and he's more or less running this-and transforming-the NYPD? What, are you kidding me? This fat freakin' transit cop in a bowtie?"
PJ: Welcome back to the show. So in late 1993, the announcement goes out that Jack Maple is going to be second-in-command of the New York City Police Department. And everyone agrees that this idea is terrible.

PJ: This is Mike Daly again. People didn't just hate Jack because he was a transit cop in a bowtie. They also hated him because he was marching around the police department telling everybody they didn't know how to do their jobs.
JACK: I walk around and I see those signs up, "Welcome to the NYPD, the greatest detectives in the world." I said, "And you guys have been running the perfect record now for 149 years. And I know you don't want to break that record. But I think it's time." I said, "You'd think we'd want to start to address crime in this fucking organization."
MUSIC
JACK: So that means that you could have your life savings stolen from you and nobody was going to investigate it. But if you were Donald Trump and you had a painting taken, we would have 30 fucking cops there. And that's outrageous. We're saying if you're poor, we're not going to investigate anything.
PJ: In his 10 years as a nobody transit cop, the thing Jack had learned more than anything was that the police department only really tried to solve one kind of crime. Press cases. Basically, stuff that happened to rich people, white people or rich white people. And you could even see this in some of the department's actual policies. Like, in Manhattan they would not investigate a burglary that was under $10,000.
MIKE DALY: And I was with him his first night, he was driving around, and he, he called operations, says, "You know anything going on?" They said, "No all's quiet commissioner." He goes, "How about any homicides? You got any homicides?" "Well, well we got two in Brooklyn." "Oh, yeah?" "Yeah." "How about the Bronx?" "Yeah. We got two there too." "Oh yeah, what about Queens?" "One." He said, "What about Manhattan?" "Well, we got one there too, but it's above 125th Street." He goes, "All's quiet though." "Yeah all's quiet, commissioner."
PJ: Crime victims who were poor, crime victims who were people of color, the crime that happened to them, in a million different ways, it was just invisible to the police department. Mike says, right in the beginning, he remembers Jack actually trying to show him how bad it was.
JACK: So we're in there about a week, and Bratton says to me, uh, "Ya know, things are pretty quiet around here." I said, "Quiet? We've had fuckin', you know, 30 murders so far. Quiet?" I said, "They don't tell you stuff. Police departments do that with commissioners."
PJ: Jack's like, this is how it happens. The police department just doesn't tell you about the crime that they don't think is important. Which is a lot of the crime. And he starts to panic as he realizes that his boss, Bill Bratton, does not understand that that's what's going on.
JACK: I wanna know every murder, um, I said, and I want to know every time we fire a gun, and we hit somebody. They said, "You know how many times that is?" I said, "Yeah."
PJ: Because police departments hate to change and most police commissioners only last for two years on the job, and so all they're gonna do is keep the bad news away from them. And what Jack wants is to force these people to take every single crime seriously. And so he calls operations back and he says, "From now on, I want a lot more information from you."
JACK: So they were cute. If a murder happened at 10:00 at night, they would wake me up here at 3 o'clock in the morning and tell me we had a murder. I liked that. You got to admire that. BRIGID: When he first became the deputy police commissioner, he became very involved. They never-like usually these people went home at night, 9 to 5 they went home. Jack was always on call. He would just show up on the scene of things. PJ: Like, you guys have like had dinner, you're going to bed and he's like, "Oh, I'm going to go out"? BRIGID: Oh, he used to do it all the time. He'd get up, and he'd be like, "Oh there's a homicide. It's right-it's only downtown, I'll be back." And he would just get up and go. [MUSIC]
PJ: He says, there were 1,946 murders last year. We were in 442 shootouts, we shot 87 people and 25 of them died. Everytime this happens, I want you to page me.
JACK: And I'm thinking about what we're gonna accomplish. And you know when you have just enough to drink, you can concentrate on one thing, right?
PJ: So late one night, Jack's left the house. And instead of going to a crime scene, he's gone to his favorite bar, Elaine's.

PJ: And he sees this thing happen. This thing he's actually seen a million times before. Elaine always has her eye on the cash register. She always knows at any given minute, are they making as much money as they should be that night, and if not, she's always answering the question "why not?" Are the waiters moving too slowly? Do they need help? Do they need to get yelled at? Like, is there some table that's taking forever in the back. Like, is there a hold up in the kitchen? She's floating around the restaurant, pushing everybody to get done what needs to get done.

And he realizes, that is what I want to have at the police department. I want all the data in my head, and I want to be able to hold people accountable like we're just a tiny family restaurant. And so he grabs a bar napkin and he scratches out a plan for exactly how he's going to do this. Next day, he comes into work holding the napkin telling everybody, "I've got it." This new system, his big plan to change the police department. A system that would come to be called CompStat.
JACK: And of course they said it couldn't be done. And do you know how much manpower we're using? It wasn't easy going the first couple of months.
Step one: every single precinct is going to track every crime that happens on a daily basis. Murders, robberies, rapes. They have to count everything. And they're going to keep this data on their own personal Charts of the Future, which they will share with Jack.
JACK: And I grabbed him on the side and I said, "Hey, fucko." I said, "If you have a problem with me, you tell me. You understand? Don't get in my way." I said, "Bob, when you and Lee Brown were here, this was the murder capital of the fucking world, you understand? I'm not going to let that happen here now." So I went on collecting the data, and we started to get it.
PJ: The old guard hated all this. One guy actually went over Jack's head, complained to Bill Bratton.

PJ: The thing was, the data was actually the easy part. The scary part of this whole plan was the meetings. Jack told all these big shot chiefs, every single week you are going to come to me. You're going to come downtown to 1 Police Plaza in the morning. They told him they couldn't make it for 9, he said that was fine, they could show up at 7. These meetings would take place in this enormous room, where one by one, the chiefs would have to walk in front of a giant screen that had all of their crime data displayed on it. In front of the podium they'd have to stand at was this large U-shaped table where every important person they were scared of would sit. And in the center of it, was Jack.
JACK: I said, "What's going on in the fifth precinct?" The borough commander looked at me and said, "A lot more heroin up there." I said, "No kidding! Really? Where is it? Where's it coming in? Who's dealing it? How do you know they're the heroin addicts that are doing these robberies? Have they been debriefed?" And he was like, "Uh."
Jack was the inquisitor. His job was to ask them questions about crime in their neighborhood and ask them what they were doing about it. These conversations went very badly. For instance, early on, Jack noticed that robberies were up in the 5th precinct. So he brought the borough commander in.
JACK: Of course, the detectives would say, "We have an active investigation." "What does that mean? Tell me exactly what it means. You got up in the morning. You went to work. What did we do to catch this guy?" They were never asked that before by a high level person. And you found out how much a number of those squad commanders didn't know.
PJ: One guy used to go to these meetings, he said it was like watching a bunch of kings get turned into peons. These guys had never been asked a follow-up question before.

PJ: Police chiefs, like 50-year-old men, would vomit in the bathroom before CompStat meetings. They would try to find friends in the department who could tip them off to see if they were up next. These were guys who lived in neighborhoods where they ran little armies of 300 men who had to obey every single one of their orders, who could never question them about anything. And now, they had to go to this other room, where they stood in front of a guy in a bowtie, surrounded by everybody they'd ever wanted to impress, 200 of their scariest peers, and they just got their lives nitpicked apart. They got asked the kind of follow-up questions you ask somebody on their first day of the job when you're convinced they know nothing. And if they couldn't answer those questions right, they were humiliated. And then they were fired. One chief told a reporter, "If they're going to keep having these meetings, they should really have us check our guns at the door."
JACK: You know, these fucking people are afraid here. We gotta do something about this. And he said to me, "You know, what do you want to make these low level cases for? We want to make the big cases." I said, "Where do you live? Where do you live? In Clarkstown or one of these places?" I said, "What did you pay? What was your house? 300,000, 350? Got a nice little police department there?
People were terrified of Jack. He told this story about berating this cop from narcotics because he found out the guy had been ignoring complaints he was getting from people who were upset about crack deals that were happening outside of their houses.
JACK: You think you'd be on the phone to the Clarkstown fuckin' cops? And would you want them arrested? And if they said to you, "Gee, don't you understand? They're just low-level guys. We're waiting for the big case. And we're going to be done with the big case in a year. You think that would be all right if your children were stepping over crack vials on their way to school?" And he said to me, I mean he was great, he said to me, "You know, you're right."
PJ: Clarkstown was a rich white suburb. Jack was saying, what if tomorrow morning, somebody was selling drugs outside your house in Clarkstown.
[MUSIC] NEWS ANCHOR 1: The city is leading the country in reducing crime.
PJ: As much as it made everybody hate Jack, CompStat worked. The crime rate in New York City plunged.
NEWS ANCHOR 2: It really is like the siege has lifted...
PJ: By the end of 1994, murders were down almost 20 percent.
NEWSCLIP: Heading out on their first patrol of the new year, the officers of Manhattan's Midtown South Precinct hit the streets with a new sense of pride and a new reputation to uphold. As keepers of the safest big city in the nation.
PJ: And they kept learning from the patterns they found in CompStat. The shooting rate is highest at 8 o'clock, right when a lot of cops are switching shifts? Fix that. Sixty percent of grand larcenies are in just 3 precincts? Find out why.
NEWSCLIP: Computers now track every crime committed in the city every day, allowing cops to react instantly to changing crime... NEWS ANCHOR: Crime is really down. POLICE OFFICER: Yes. NEWS ANCHOR: This is not some kind of fudging of numbers. POLICE OFFICER: No. NEWS ANCHOR: This is not a seasonal thing. POLICE OFFICER: It's not only statistically recognizable, you can feel it in the sense of the city. The sense of menace has ebbed.
PJ: By the end of '95 felony crime was down 39 percent. The murder rate had plunged down back to where it was in the early '70s. The biggest drops in homicides came in the neighborhoods that the cops were used to ignoring. One reporter wrote, "You could fill Madison Square Garden with all the would've been dead people who are alive simply because Maple figured it out"

PJ: When Commissioner Bratton left New York City in '96, Jack followed him out the door.

People thought they were geniuses. There were academics who argued that it wasn't CompStat that saved New York, that the crime rate went down for other reasons. Jack would tell 'em, "Come to my office with a big stack of cash, all your grant money. I'd love to take a bet on that.

For the next few years, Bill and Jack went everywhere, across America, across the world, spreading CompStat to police departments. People thought of them as revolutionaries.

Jack died in 2001.

Already by then, some of the problems with the system had started to crop up, but most people hadn't noticed. A few years later, it would be clear -- the flaw in the great crime fighting machine.
CREDITS
Next time on Reply All: what went wrong.

Reply All is hosted by me, PJ Vogt, and Alex Goldman. The show is produced by Sruthi Pinnamaneni, Phia Bennin, Damiano Marchetti, Anna Foley, Simone Polanen, Jessica Yung and Kaitlin Roberts. Our editor is Tim Howard. Our intern is Heather Schröering. More editing help this week from Alex Blumberg. We were mixed by Rick Kwan and Kate Bilinski. Fact-checking by Michelle Harris.

Special thanks this week to Chris Mitchell, Saki Knafo and Khrista Rypl.

Our theme song is by the mysterious Breakmaster Cylinder. Original music in this week's episode by Tim Howard and Breakmaster Cylinder. And thanks to our additional musicians Anja Krieger on flute and Michael Brownell on upright bass. Recording help from Mark Lewis.

Matt Lieber is 10 hours of sleep.

You can find more episodes of the show on iTunes, Spotify, or wherever you get your podcasts. Thanks for listening. You can hear our next episode right now.

# data[].attributes.attachment_link.title
#127 The Crime Machine, Part I | Reply All

# data[].attributes.attachment_link.description
New York City cops are in a fight against their own police department. They say it's under the control of a broken computer system that punishes cops who refuse to engage in racist, corrupt policing. The story of their fight, and the story of the grouchy idealist who originally built the machine they're fighting.

# data[].attributes.body
The European Union met in Brussels to discuss laws and regulation regarding AI technologies. Google, Apple, and Facebook showed up to meet with members of the EU. The EU recognizes the benefits that AI can have but want to have measures in place to avoid the negative consequences. Margrethe Vestager, executive vice president of the European Commission said that "there must be privacy protections, rules to prevent the technology from causing discrimination, and requirements that ensure companies using the systems can explain how they work..."
https://www.nytimes.com/2020/02/16/technology/europe-new-AI-tech-regulations.html?te=1&nl=morning-briefing&emc=edit_NN_p_20200217&section=topNews&campaign_id=9&instance_id=16053&segment_id=21347&user_id=629e7df37cf10effc49d589fed3b5fc7&regi_id=110069546tion=topNews

# data[].attributes.body
DuckDuckGo: https://duckduckgo.com/?va=z&t=hk (a search engine that doesn't track you)

Obfuscation:
AdNauseam: https://adnauseam.io/ (a browser extension that clicks all the ads for you)

TrackMenot: https://trackmenot.io/ (a browser extension that is constantly searching for random terms in the background)

# data[].attributes.attachment_link.description
The world's most popular autocomplete game. How does Google autocomplete this search? "Our new obsession." - TIME

# data[].attributes.attachment_link.readable_text
Try these neural network-generated recipes at your own risk.

Stuck in a rut in the kitchen? Tired of preparing sandwiches the same old way?

Machine learning can help!

I trained a neural network on over 30,000 examples of cookbook recipes, and it learned to produce new recipes of its own. You can learn more about the training process, and watch it learn to generate new recipes here.

They aren't good recipes, though. In fact, almost all of them are terrible. I made one of them once, and now I still cringe at the faintest whiff of horseradish. SuperDeluxe made another of them, but at least they are professionals and were wise enough not to eat any.

Here for your entertainment I give you several more recipes the neural network has generated, with the caveat that if you should try to prepare or, god forbid, actually consume one of these, I am absolutely not responsible for the consequences.

Small Sandwiches
dish, chili, lemon, salads, seafood

½ cup shortening
1 cup snow peas and cut into ¼ inch cubes
1 1 inch
15 oz peach halves,remaining posting
1 salad dressing
½ cup barley
2 large bones sliced chicken salmon:
1 cup cheddar cheese; grated
4 each onions and cut into 8 servings
2 cup chicken stock or mayonnaise
2 tablespoon brown sugar
½ cup cream cheese; softened
¼ cup grated cheddar cheese
8 oz mashed potato fillets
3 tablespoon coarsely chopped green onion
1 minced fresh sage leaves
1 drained bean sprouts

--LAKE CHOICE--

1 pinch salt
3 bay leaves
2 garlic cloves
1 dash black pepper
6 large garlic cloves
¼ cup along with
½ teaspoon vegetable oil
¼ teaspoon salt, optional
¼ teaspoon white pepper
4 teaspoon sugar
3 tablespoon peanut butter
½ cup coconut

--FILLING--

1 cup minced season tomatoes
2 cup hot water
¼ cup vegetable oil
2 cup all-purpose flour
2 tablespoon the seasoned salt
1 cup margarine, melted
1 lb jumbo shrimp
1 or freshly ground black pepper
1 up
1 thai shrimp; finely chopped
1 garlic clove, minced

Mix all ingredients except cheese and process 1 hour. Pour over ribs.

Cover and bake for 30-35 minutes. Serve with warm milk and marinade distributed; prepare the bottoms.

Watch the end of the fillets to the heat and set in a bowl and heat at this low for 5 minutes, until softened. Top with a little the next 2 ingredients; spoon the one day, 1 ½ hours. Take an and inverting it and turn the center. Let cool in the pan on wire rack. To serve cooking time: It is been ribsotro. while the serving is alternatively rich will puree in the miquinally preparing gravy. They should seal.

Yield: 4 servings

Beothurtreed Tuna Pie
pastries, fruits, pork

1 hard cooked apple mayonnaise
1 onion
3 tablespoon butter
5 cup lumps; thinly sliced
½ cup chicken broth
1 carrot, spinach (vanilla estach w/pecans)
1 freshly ground black pepper - optional

Surround with 1 ½ dozen heavy water by high, and drain & cut into ¼ in.
remaining the skillet.

Pour liquid into thin baking pan.

Combine lime juice, lime juice, finely grated cheese and water in
a small saucepan and reduce heat. Cover and simmer about 20
minutes at medium-high speed until thickened.

Yield: 4 servings

Tart Cover Shrimp Butter Wol
seafood

½ cup catsup
1 teaspoon cornstarch
½ cup lemon juice
1 teaspoon cumin seeds
1 can fried pale fruit to cover that drain
¼ lemon
1 fresh parsley for garnish

WINE-POCKED COST WITH PUDDING. KEEP WARM. Heat oil in large skillet over medium-high heat, add green meat. Season with salt and pepper boiled coated mixture. Sprinkle remaining ground beef in greased 9-inch pie plate. Lay border fillet on layer of custard.

Topping: In small skillet, cook onions and celery until potatoes are done, about 3 minutes. Serve hot.

Yield: 1 servings

Good Ponesed Dressing
deserts

--TOPPING--

4 cup cold water or yeast meat
½ cup butter
¼ teaspoon cloves
½ cup vegetable oil
1 cup grated white rice
1 parsley sprigs

Cook the onions in oil, flour, dates and salt together through both plates.

Put the sauce to each prepared Broiler coated (2 10" side up) to lower the fat and add the cornstarch with a wooden toothpick hot so would be below, melt chicken. Garnish with coconut and shredded cheese.

Source: IObass Cindypissong (in Whett Quesssie. Etracklitts 6) Dallas Viewnard, Brick-Nut Markets, Fat. submitted by Fluffiting/sizevory, 1906. ISBN 0-952716-0-3015 NUBTET 10 , 1972mcTbofd-in hands, christmas charcoals Helb & Mochia Grunnignias: Stanter Becaused Off Matter, Dianonarddit Hht

5.1.85 calories CaluAmis

Source: Chocolate Pie Jan 584

Good Wine Drained Chili
meats, chicken, low cal

4 quart milk
3 cloves garlic, finely chopped
2/3 cup chocolate chips
1 teaspoon cornstarch
1 wine, mashed
12 oz kettle (garnished" powder, cinnamon
3 tablespoon paprika

Heat broiler in warm place for 15 minutes.

From: Martha Stewart Living/Marketing Orden GRande N... Sc. (115) on serving and produce id the Backdacake: Typed for you by Schan Scandet Refrigerate by Mary Herther, Amarant, Miry's to the Markning Cinnamon Marthan Sear Foods county Typis by Matiersr : Turtlean Wo Genie Alse

From: Quick Times with frosting.

! AUTRIMANONISG ROBLIME Tomethe Aspic ead Savor Kr Confit. - Vegetarian Brunding

Yield: 6 servings

Want more? You can find all my other neural network recipe experiments here.

Want more than that? I've got a bunch more recipes that I couldn't fit in this post. Enter your email here and I'll send you 38 more selected recipes.

Want even more? Check out Tom Brewe's list, which inspired my own neural network recipe experiments. May I direct you in particular to the shredded bourbon?

# data[].attributes.attachment_link.title
Try these neural network-generated recipes at your own risk.

# data[].attributes.attachment_link.description
Stuck in a rut in the kitchen? Tired of preparing sandwiches the same old way? Machine learning can help! I trained a neural network on over 30,000 examples of cookbook recipes, and it learned to produce new recipes of its own. You can learn more about the training process, and watch it learn to generate new recipes here.

# data[].attributes.body
This article brings attention to the fact that Twitter uncovered Clearview AI, which we discussed in class, was taking information and pictures from the social media site without their knowing consent. They served Clearview AI with cease and desist papers to protect it's users.

# data[].attributes.attachment_link.readable_text
In a cease-and-desist letter sent this week, Twitter told Clearview AI it is violating its policies by collecting data for its facial recognition software, a Twitter official confirmed to The Hill on Thursday.

The development was first reported by The New York Times.

The cease and desist letter reportedly calls on Clearview AI to stop taking data from Twitter "for any reason" and delete any that was previously collected.

Twitter prohibits users from using its platform for "facial recognition," on its restricted uses page.

The New York Times reported over the weekend that Clearview, which claims to have a database of more than 3 billion photos, is working with more than 600 law enforcement agencies in the last year.

Clearview's tool scans faces to identify individuals and can help law enforcement catch suspects.

Law enforcement officials told the Times Clearview's facial recognition app helped them identify suspects in criminal cases.

Clearview amassed its database from photos on social media sites, including Twitter, Facebook, YouTube and Venmo, according to the Times.

Tor Ekeland, a lawyer for Clearview, told the Times the company received the letter and "will respond appropriately."

An official for Clearview was not immediately available for comment.

The company is also facing questions from lawmakers over its facial recognition software.

Sen. Ed Markey Edward (Ed) John Markey Warren asks for probe of whether Trump violated law by delaying Puerto Rico funds This week: Trump's budget lands with a thud on Capitol Hill What the impeachment vote looked like from inside the chamber MORE (D-Mass.) on Thursday sent a series of questions to Clearview's CEO after reports that the company had been selling its facial recognition software to law enforcement.

Markey said that the technology has "alarming potential to impinge on the public's civil liberties and privacy."

# data[].attributes.attachment_link.title
Twitter tells facial-recognition app maker to stop collecting its data

# data[].attributes.attachment_link.description
In a cease-and-desist letter sent this week, Twitter told Clearview AI it is violating its policies by collecting data for its facial recognition software, a Twitter official confirmed to The Hill on Thursday. The development was first reported by The New York Times.

# data[].attributes.headline
Potential Facial Recognition Ban for Law Enforcement - Meg Cory

# data[].attributes.subject
Potential Facial Recognition Ban for Law Enforcement - Meg Cory

# data[].attributes.body
Following the information that came out about Clearview AI senators have proposed a bill that will ban facial recognition software to be used by law enforcement. Mentioning in the bill how the software targets minority groups.
https://www.cnet.com/news/facial-recognition-could-be-temporarily-banned-for-law-enforcement-use/

# data[].attributes.body
This soap dispenser is unable to work when the hand beneath it is dark. This goes to raise the question of why technology companies are not testing their products on different people or hiring more diverse people to develop the product, as well as what other technologies are flawed like this.

# data[].attributes.body
Surveillance cameras are used in both public areas and private homes. They are somewhat visual and people are aware about the whereabouts. However, there are manufactures that produces cameras that are can be masked as everyday products, making it much harder to find. These hidden cameras can take shape as pens, clocks, photo-frames, etc. Although there are ways to find what is a real product and what is a camera, not too many people are aware of the placements of the products within rent out places such as motels and especially AirBNBs.

https://www.amazon.com/s?k=hidden+cameras&ref=nb_sb_noss_1

https://www.nbcsandiego.com/news/local/airbnb-guests-find-hidden-cameras-in-bathroom-bedroom-lawsuit/2109603/

# data[].attributes.body
Emails from an Amazon employee to Immigration and Customs Enforcement's Homeland Security Investigations team show meeting and interest in the tech giant's real-time face-scanning product "Rekognition."

# data[].attributes.attachment_link.authors[].name
Andrea Peterson (Project on Government Oversight)

# data[].attributes.attachment_link.description
Emails from an Amazon employee to Immigration and Customs Enforcement's Homeland Security Investigations team show meeting and interest in the tech giant's real-time face-scanning product...

# data[].attributes.attachment_link.title
This is everywhere in the country facial recognition is happening and what you can do about it.

# data[].attributes.attachment_link.description
This interactive map shows where facial recognition surveillance is happening, where it's spreading to next, and where there are local and state efforts to rein it in. See something missing? Contact us: [email protected]

# data[].attributes.body
Hong Kong protests over the last year have people feared for their safety once cameras and surveillance measures have been taken to combat the protests. This has forced civilians to destruct and distract from the surveillance & fight back. https://www.youtube.com/watch?v=32KTKXZZ-BI

# data[].attributes.body
Google Glass is a wearable computing device, which comes with a head-mounted display. This smart device displays information to users in a hands-free format and also enables them to interact with the Internet via voice commands, while on the go.

# data[].attributes.attachment_link.description
Thank you for over a decade of innovation and partnership. As of March 15, 2023, we will no longer sell Glass Enterprise Edition. We will continue supporting Glass Enterprise Edition until September 15, 2023. For more information visit our Help Center.

# data[].attributes.headline
Tyler Pray: Elon Musk's Neuralink Brain Implant 

# data[].attributes.subject
Tyler Pray: Elon Musk's Neuralink Brain Implant 

# data[].attributes.body
This implant has flexible electrode "threads" which are implanted in brain. These threads detect and record the electrical signals in the brain, and transmit this information outside the body.
Elon Musk hopes to use this implant to treat different brain and spine disorders along with creating symbiosis with humans and AI.

https://www.neuralink.com

https://www.youtube.com/watch?v=mty_WVP8DvA

# data[].attributes.body
This article talks about how lawmakers in Washington want to regulate data privacy as well as facial recognition.

"State and local government agencies are prohibited from using facial recognition for ongoing surveillance without a warrant or threat of imminent danger."

" Microsoft was a key player in the effort to enact privacy regulations in Washington state last session.

https://www.geekwire.com/2020/washington-state-lawmakers-debut-legislation-consumer-privacy-facial-recognition/

# data[].attributes.attachment_link.readable_text
Washington state legislators will push for new regulations governing data privacy and facial recognition next year, and a draft bill obtained by GeekWire provides a glimpse into what they have in store.

The new legislation builds on a bill that passed the Senate before dying in the House last session. The bill's sponsors say they are taking lessons from that experience, though several of the sticking points that contributed to the last bill's demise remain.

In their second attempt, Washington Democrats hope to give consumers new rights to ownership over their data, establish new transparency requirements for companies that process consumer data, and implement new safeguards for facial recognition technology.

"The guiding principles are very similar but I made a real effort to listen to some of the concerns about the need for tighter definitions, the need for clearer lines around enforcement," said Sen. Reuven Carlyle, the bill's sponsor. He said the new version more clearly "articulates the fundamental right of consumers to access their data, to correct their data, to delete their data."

If the bill is enacted, Washington will follow California and the European Union, both of which have established their own privacy regulations. Washington would be a pioneer in regulating facial recognition, as the controversial technology is not explicitly covered by California or the EU's laws. As home to two of the most powerful technology companies in the world, Amazon and Microsoft, Washington's new regulations could have implications that extend far beyond the state's borders.

Here's a rundown of some of the key provisions in the draft bill:

Consumers have the right to access, delete, correct, and move their data.

Consumers can opt-out of data collection.

Companies that control consumer data must be transparent about the information's use, minimize its collection, and specify the purpose for collecting it.

Companies that control consumer data must conduct regular risk assessments.

State and local government agencies are prohibited from using facial recognition for ongoing surveillance without a warrant or threat of imminent danger.

Companies that make facial recognition software must allow third-party testing for accuracy and bias.

Institutions using facial recognition in public spaces must obtain consent.

Government agencies in the state that want to use facial recognition must publish accountability reports and establish data management policies.

The governor's office can create a task force to report on potential abuses and threats of facial recognition technology.

The attorney general has the authority to sue companies that violate this bill but individual consumers do not.

Microsoft was a key player in the effort to enact privacy regulations in Washington state last session. The Redmond, Wash., software giant is a vocal advocate for data privacy laws and facial recognition in Washington and beyond. Microsoft said this month that it will make changes required by California's new data privacy law available to all its U.S. customers.

Amazon is coming around to the idea. In September Amazon CEO Jeff Bezos said the company's public policy team is working on a set of proposed regulations for facial recognition technology. It's a shift from 2018, when Amazon attorney Andrew DeVore warned U.S. senators about the risks of onerous privacy regulations during a public hearing.

Both Microsoft and Amazon develop facial recognition technology and handle an enormous amount of consumer data. The regulations outlined in Carlyle's draft bill would apply to both companies, and any other business that controls or processes personal data of 100,000 consumers or more.

The rules apply to companies located in Washington and companies that target services to Washington customers. Businesses that derive more than 50 percent of their revenue from the sale or processing of personal data are also subject to the regulations, even if they have fewer than 100,000 customers.

One point of contention during the debate over last session's bill has to do with what's called the "private right of action." That's the ability for individual consumers to sue companies that violate the rules outlined in the bill. That right was not included in the original bill last session and it's not in the new draft either. If enacted, only Washington's attorney general could sue companies for violating the law, with some exceptions.

"A blanket private right of action, when it comes to technology, is a slippery slope," said Sen. Joe Nguyen, a co-sponsor of the bill who also works full-time for Microsoft. "That's why they have it under the AG's office."

Nguyen said that giving individuals the authority to sue over privacy violations would be too burdensome for smaller companies without the resources to field those legal challenges. As a senior program manager at Microsoft, Nguyen is in a unique position. He's helping to craft what could become Washington's new data privacy laws and he helped a major tech company implement the European Union's new rules, known as the General Data Protection Regulation.

"It was a year-long process, completely engaged the engineering team, and the cost associated with it was astronomical, relatively speaking," Nguyen said. "In order to put some of these things in place is not easy."

Nguyen also plans to spin out the facial recognition regulations as a separate bill in an effort to ensure they move forward, even if the privacy bill stalls.

"I'm very passionate about making sure something for facial recognition is in place, because I believe it is impacting communities right now, and I don't want it to be caught up in any potential political fight," Nguyen said.

A data governance workgroup in Portland last week unveiled a draft ordinance prohibiting city government agencies from acquiring or employing facial recognition technology. A companion ban on privately owned use of the technology in public spaces also is in the works.

Carlyle and Nguyen are quietly soliciting feedback on the draft bill before formally introducing it. Washington's new legislative session begins Jan. 14.

Celebrate the innovators, entrepreneurs and top leaders in tech at the annual GeekWire Awards. Heavy appetizers, signature cocktails, wine, and beer will be served. Join us at MoPOP on March 26 as we reveal winners in 13 categories, including Startup of the Year, Innovation of the Year and Next Tech Titan. Early-bird tickets on sale now!

# data[].attributes.attachment_link.title
Sneak peek: How Washington state lawmakers plan to regulate data privacy and facial recognition

# data[].attributes.attachment_link.description
Washington state legislators will push for new regulations governing data privacy and facial recognition next year, and a draft bill obtained by GeekWire provides a glimpse into what they have in store. The new legislation builds on a bill that passed the Senate before dying in the House last...

# data[].attributes.headline
Can the digital future be our home? We question ourselves about our role in coexisting with machines, question of what will happen to us if machines replace us. Where do we go? Intro has a heavy focus of what our home is and reintroduces the topic of technological utopianism. Ex. Ford Motors and Detroit.

# data[].attributes.subject
Can the digital future be our home? We question ourselves about our role in coexisting with machines, question of what will happen to us if machines replace us. Where do we go? Intro has a heavy focus of what our home is and reintroduces the topic of technological utopianism. Ex. Ford Motors and Detroit.

# data[].attributes.body
""Are we all going to be working for a smart machine, or will we have smart people around the machine?" and
Now the disruptions . of the twenty-first century have turned these exquisite anxieties and longings of dislocation into a universal story that engulfs each one of us"

# data[].attributes.body
This chapter focuses on the definition surveillance capitalism; involving details with the moral aspect of surveillance, how it’s operated, and the harm that comes from it.

“Surveillance capitalism unilaterally claims human experience as free raw material for translation into behavioral data.”

“Surveillance capitalists know everything about us, whereas their operations are designed to be unknowable to us.”

# data[].attributes.body
The aware home was supposed to be a ubiquitous system but became something that was not private at all.

“Each thermostat comes with a “privacy policy” a “terms-of-service agreement” and a “end-user licensing agreement”

“It collects data about its uses and environment. It uses motion sensors and computation to ‘learn’ the behaviors of a home’s inhabitants”

“Today these rights to privacy, knowledge, and application have been usurped by a bold market venture powered by unilateral claims to others’ experience and knowledge that flows from it”

# data[].attributes.body
The reason surveillance capitalism is so big and the rate that it got so big is because we didn't know what was to come of it. Because it is unprecedented no one knew how to handle it or how to contest it.
"One explanation for surveillance capitalism's many triumphs floats above them all: it is unprecedented."
"The unprecedented nature of surveillance capitalism has enabled it to elude systematic contest because it cannot be adequately grasped with out existing concepts"

# data[].attributes.body
Summary: Technology is not the problem it is the people behind it that know how surveillance capitalism works and that control it. We like to blame the technology and act as if that is what's responsible, but it really comes down to the people that want to make money.

-"technology illusion"

Quotes:

"That surveillance capitalism is a logic in action and not a technology is a vital point because surveillance capitalists want us to think that their practices
are inevitable expressions of the technologies they employ ."

"In truth, search engines do not retain, but surveillance capitalism does."

"Surveillance capitalism is not an accident of overzealous technologists, but rather a rogue capitalism that learned to cunningly exploit its historical conditions to ensure and defend its success."

# data[].attributes.body
Kashmir Hill and Surya Mattu's article about their smart home experiment.

Here's the TEDTalk we watched in class: https://www.ted.com/talks/kashmir_hill_and_surya_mattu_what_your_smart_devices_know_and_share_about_you

# data[].attributes.attachment_link.readable_text
In December, I converted my one-bedroom apartment in San Francisco into a "smart home." I connected as many of my appliances and belongings as I could to the internet: an Amazon Echo, my lights, my coffee maker, my baby monitor, my kid's toys, my vacuum, my TV, my toothbrush, a photo frame, a sex toy, and even my bed.

"Our bed?" asked my husband, aghast. "What can it tell us?"

"Our breathing rate, heart rate, how often we toss and turn, and then it will give us a sleep report each morning," I explained.

"Sounds creepy," he said, as he plopped down on that bed, not bothered enough to relax instead on our non-internet-connected couch.

I soon discovered that the only thing worse than getting a bad night's sleep is to subsequently get a report from my bed telling me I got a low score and "missed my sleep goal." Thanks, smart bed, but I know that already. I feel like shit.

Why? Why would I do this? For convenience? Perhaps. It was appealing to imagine living like the Beast in the Disney movie, with animated objects around my home taking care of my every need and occasionally serenading me. As a result of the apartment upgrade, I could watch what was happening in the house when we weren't there. I could use voice commands to turn on the lights, coffee maker, and music. I could exchange voice messages with our toddler (and her caregiver) through a toy. I got reminders from my toothbrush to brush and tips on how best to do it. If I got cold in the night, my bed could warm me up. And I no longer had to push a vacuum around the house, instead activating a robot to do it for me with a press of a smartphone button.

Thanks to the Internet of Things, I could live in my very own tech-mediated Downton Abbey. That's the appeal of smart homes for most people, and why they are supposed to be a $27 billion market by 2021. But that wasn't my primary motivation. The reason I smartened up my house was to find out whether it would betray me.

The reason I smartened up my house was to find out whether it would betray me.﻿

I installed internet-connected devices to serve me, but by making the otherwise inanimate objects of my home "smart" and giving them internet-connected "brains," I was also giving them the ability to gather information about my home and the people in it. The company that sold me my internet-connected vacuum, for example, recently said that it collects a " rich map of the home " and plans to one day share it with Apple, Amazon, or Alphabet, the three companies that hope to dominate the smart home market. Once I made my home smart, what would it learn and whom would it tell?

One person I knew it would be leaking to was my colleague, Surya Mattu, because he built a special router to monitor the devices monitoring me.

Yes, I am basically Kashmir's sentient home. Kashmir wanted to know what it would be like to live in a smart home and I wanted to find out what the digital emissions from that home would reveal about her.Cybersecurity wasn't my focus. (I wasn't interested in hacking her sex toy or any of her other belongings.) Privacy was. What could I tell about the patterns of her and her family's life by passively gathering the data trails from her belongings? How often were the devices talking? Could I tell what the people inside were doing on an hourly basis based on what I saw?

Using a Raspberry Pi computer, I built a router with a Wi-Fi network called "iotea" (I'm not very good at naming things) to which Kashmir connected all of her devices, so that I could capture the smart home's network activity. In other words, I could see every time the devices were talking to servers outside the home.

I had the same view of Kashmir's house that her Internet Service Provider (ISP) has. After Congress voted last year to allow ISPs to spy on and sell their customers' internet usage data, we were all warned that the ISPs could now sell our browsing activity, or records of what we do on our computers and smartphones. But in fact, they have access to more than that. If you have any smart devices in your home-a TV that connects to the internet, an Echo, a Withings scale-your ISP can see and sell information about that activity too. With my "iotea" router I was seeing the information about Kashmir and her family that Comcast, her ISP, could monitor and sell.

There was a lot to see. Since the router was set up at the beginning of December, there hasn't been a single hour of complete silence from it, even when there was no one in the house.

After a week of living in my newly smartened home, I could tell why the Beast was always in such a bad mood: The animate objects in my home were becoming a constant source of annoyance. I thought this was going to be a story about privacy, but instead I was finding out how infuriating it is to live in a janky smart home.

Our 1970s apartment building did not offer enough electrical outlets for this 2018 smart home, so we had power strips and outlet expanders everywhere, to the point where I was worried I was going to spark a fire and burn our smart home down. (This actually might have been cathartic.)

I had to download 14 different apps to my phone to control everything, which meant creating an account for each one of those apps. (Yes, my coffeemaker has a log-in and a very long terms of service agreement.) After setting them up, I thought I'd be able to control all the devices by issuing voice commands to Alexa via the Echo-the smart speaker that we've been using for the last year as a glorified timer and music player - but this did not go as well as I had hoped.

The fantasy of the smart home is that it will save us time and effort.

It took at least two hours to get all of our Christmas lights plugged into smart plugs from WeMo and Sonoff, and then to get those plugs online with their apps, and then to get those apps to talk to the Alexa app. The first night I said, "Alexa, turn on the Christmas lights," they all turned on in sparkly synchronicity and it was magical. But one day, Alexa stopped recognizing "Christmas lights" as a group, and I could not figure out how to fix it, so I had to ask Alexa each night to turn off the lights one-by-one. ("Turn off kitchen Christmas lights." "Turn off living room Christmas lights." "Turn off bookcase lights.") This was way more annoying than turning them off manually. The fantasy of the smart home is that it will save us time and effort, but the friction involved in getting various devices from different companies to work together meant that many things took longer to do.

I could tell when the lights were being turned on and off. And even when Kashmir's family wasn't using them or weren't home, those smart plugs were constantly talking to their home servers. One of them checked in erratically, around four times per day at random intervals. The other smart plug, that promised insights about how much electricity you were using, was far more chatty, pinging home almost every hour.

Smart coffee was also a world of hell. The Brewgenie Smart Coffeemaker I ordered first was "smart" in that it had Bluetooth connectivity, so I could use its custom app on my phone to make it run a pot of coffee, but it wasn't compatible with Echo, so I couldn't say, "Alexa, make me coffee." To remedy this, I ordered the Behmor Connected Customizable Temperature Control Coffee Maker which promised Alexa compatibility. I came to regret this while setting up my Eight Sleep Tracker-a sensor layer that went over my mattress that could track sleep, warm the bed, and connect to "any wifi-enabled device in your house." Its instruction manual, along with dire warnings about possibly setting my bed on fire, informed me that it could automatically brew me coffee when it sensed I was waking up ... but only if I had a WeMo coffeemaker.

I refused to order a third coffee maker for my smart home.

I made do with the Behmor, which makes incredible coffee but had a prickly relationship with Alexa. Each morning, my husband and I begged Alexa to put the coffee on. She would only react to a very specific phrasing of the request and then would only do so sometimes.

"Ask Behmor (pronunciation: Be-more) to brew me coffee."

"Behmor," she would respond. "A passion for coffee. How can I help you?"

"Brew me coffee."

"I don't understand," she would respond. This was especially aggravating for two caffeine-addicted people who had not yet had their coffee. Sometimes we would keep rephrasing the question until she got it, but more often, one of us would just get up, walk to the kitchen and press the button on the coffeemaker rather than doing it the "smart" way.

And that's when I ran into the next issue: the Withings Home Wi-Fi Security Camera with Air Quality Sensors that I had set up in our living room. When the camera detects motion or noise, it automatically records what it's seeing. That's great if you're worried about break-ins or how people treat your kid when you're not around, but not great for protecting the intimacy of your home. The day after I set it up, it caught me walking through the living room naked, resulting in the very first nude video of me (that I know about), which was promptly sent to the cloud and saved to the Home Cam app on my phone. This appears to be a common problem for the smart home set.

The camera was constantly sending huge amounts of data, which makes sense given it's sending video. But good news-it was all encrypted, so someone monitoring your network will not get access to what the camera sees, including nude videos.

"You set up a camera!??" my husband asked, horrified. Four clicks deep into the app, I found out it keeps everything it records by default for two days but that you have to sign up for a "premium account" if you want to save anything longer.

"It deletes the video after two days," I told my husband.

"How comforting," he replied, without looking up from his phone, his voice doing the eye roll for him.

When I went away to Spain for a few days for work in December, I was looking forward to being able to check in on my family via the camera. But eight hours after I left, during my layover in Toronto, I opened the app on my phone and it reported back that the camera was offline; the most recent video was from that morning, showing my daughter in my husband's arms in the kitchen.

"The camera's not working," I texted my husband.

He replied that he had unplugged it.

"It was staring at me while I made coffee," he texted back.

I told him I needed it plugged in for the story to monitor the data flows and he said he would do it. But as the days passed in Spain it remained offline and I stopped bothering him about it. (The acceptance of complete surveillance had not been part of our marriage vows.)

When I got back from Europe, I moved the camera to the nursery. It would be more useful there, and thankfully our 1-year-old daughter is too young to care about privacy invasions. The downside of this was that the move somehow screwed up the camera's power cord, so it started randomly losing its connection to the router; when that happened, the camera would glow bright orange to inform me that it was offline. This is not a great design for a camera billed as a baby monitor, as the bright orange light in the otherwise dark nursery would then wake my daughter.

The whole episode reinforced something that was already bothering me: Getting a smart home means that everyone who lives or comes inside it is part of your personal panopticon, something which may not be obvious to them because they don't expect everyday objects to have spying abilities. One of the gadgets-the Eight Sleep Tracker-seemed aware of this, and as a privacy-protective gesture, required the email address of the person I sleep with to request his permission to show me sleep reports from his side of the bed. But it's weird to tell a gadget who you are having sex with as a way to protect privacy, especially when that gadget is monitoring the noise levels in your bedroom.

The Eight Sleep tracker sent its data through a nonstandard port that I wasn't monitoring, so I wasn't able track what was happening in the bedroom.

One of the decisions we made early in the process was not to break the encryption on the devices in Kashmir's house. We wanted them in their normal, off-the-shelf state. We'll leave the encryption-breaking to the growing number of computer-science academics who are building their own smart homes to try to figure out the future of corporate surveillance.

When the data streams were unencrypted, which was the case every time someone watched Hulu on the Vizio smart TV, I could see exactly what was being sent. When they were encrypted, as the majority of the data turned out to be, I could see only the metadata-the volume of data being sent and to where, which is like seeing the outside of an envelope but not being able to read the letter inside. But sometimes, metadata is the message. I know, for example, when the family wakes up, because the Amazon Echo usually starts playing songs from Spotify between 6 a.m. and 8 a.m., even if I don't know which songs. I also know that Kashmir likes to use the Alexa Sounds app -which loops ambient sounds such as rain, oceans, and fireplaces-between 6 p.m. and 8 p.m., which is when she puts her 1-year-old daughter to sleep.

Getting a smart home means that everyone who lives or comes inside it is part of your personal panopticon.

It turns out that how we interact with our computers and smartphones is very valuable information, both to intelligence agencies and the advertising industry. What websites do I visit? How long do I actually spend reading an article? How long do I spend on Instagram? What do I use maps for? The data packets that help answer these questions are the basic unit of the data economy, and many more of them will be sent by people living in a smart home.

The thing in the smart home that most fascinated me, because of its value to advertisers, was the television. In Kashmir's house, it doesn't get turned on every day, but when it does, it's usually between 8pm and midnight. A typical day of TV viewing looks like this in the data:

An outlier to this trend was Christmas Day, when, it looks like, the television was on throughout the day. I couldn't tell if it was for watching a show or streaming music, but there was a consistent stream of activity.

I didn't like it, but the TV was tuned to basketball for most of the day on Christmas. Thanks for the reminder, Surya/smart home!

Similarly, it looks like 2017 ended with a low-key TV night:

Okay, yes, we stayed in on New Year's Eve. Don't shame me. We have a baby! We watched Phantom Thread on DVD. There was no internet involved. Why do you even know that?

Even though the "smart" part of the TV wasn't being used that night, it was still sending data about its use.

When the TV is on, it's usually tuned to Netflix or Hulu. I couldn't see what they watched on Netflix because Netflix encrypts streams. But I discovered that Netflix doesn't encrypt images, so I could see the shows being recommended to them, which is revealing in that it shows what Netflix thinks they should like:

Meanwhile, Hulu sends its traffic unencrypted, so I could spy on exactly what they watched, like when Netflix spied on A Christmas Prince addicts. I was able to figure out which nights they were binge-watching Difficult People on Hulu (which, based on the traffic, seems to be one of their favorite shows).

I wasn't the only one watching what they were watching. Their TV is telling Scorecard Research, a digital behavior tracker, and Rewardtv.com, a website owned by Nielsen, what they stream on Hulu.

Here's what the reward TV request looked like; it included the name of the episode in plain text:

http://pt.rewardtv.com/otif.do?tfid=301&cp=soc&bcr=Hulu.com&pgm=Difficult%20People&plt=TV&seg=1&sid=1000046&epi=Season%201%20-%20Devil's%20Three-way&r=0.5704269525658884

And this is the scorecard research tracking request:

http://b.scorecardresearch.com/b2?rn=05762658&c1=1&c2=3000007&c3=None&c4=3000007&c5=010201&c6=Difficult%20People-1&c13=a48d3b6aa0a8&c14=TV&c15=726bcb7500438f0bbf8f002504f49acf&c16=1&ca1=3&ca2=3000007&ca5=0201﻿

Hulu's decision not to encrypt streams means people who watch its shows can have their viewing habits tracked by third parties. (Hulu did not respond to a request for comment about its lack of encryption.)

"Surya wants to know who is binging on Difficult People," I told my husband, the responsible party.

"Our TV is watching us?" he asked, surprised, even though he'd been the one to connect the TV to the router surveilling us. "Wow, I forgot."

This must be what it's like to be in a documentary or in a reality TV show. The cameras eventually move to the periphery of your vision and then disappear altogether. If homes become sentient, and it becomes the norm that activity in them is captured, measured, and used to profile us, all of the anxiety you currently feel about being tracked online is going to move into your living room.

All of the anxiety you currently feel about being tracked online is going to move into your living room.

Talking to the human who actually got to see and analyze my smart home's activity made me realize just how deeply uncomfortable it is to have that data pooled somewhere.

After two months of data collection, I was able to pick up a bunch of insights into the Hill household-what time they wake up, when they turn their lights on and off, when their child wakes up and falls asleep-but the weirdest one for me personally was knowing when Kashmir brushes her teeth. Her Philips Sonicare Connected toothbrush notifies the app when it's being used, sending a distinctive digital fingerprint to the router. While not necessarily the most sensitive information, it made me imagine the next iteration of insurance incentives: Use a smart toothbrush and get dental insurance at a discount!

The larger pattern that emerged about the smart home was that all of the devices phoned home daily, even if they hadn't been used, telling the companies that made them, "Hey. I'm still here. I've still got power. Have any updates for me?"

An exaggerated version of this was seen in the Echo and Echo Dot, which were in constant communication with Amazon's servers, sending a request every couple of minutes to http://spectrum.s3.amazonaws.com/kindle-wifi/wifistub-echo.html. Even without the "Alexa" wake word, and even when the microphone is turned off, the Echo is frequently checking in with Amazon, confirming it is online and looking for updates. Amazon did not respond to an inquiry about why the Echo talks to Amazon's servers so much more frequently than other connected devices.

The funniest "conversation" that happened over the two months was a week in January when Kashmir was out of town. I could tell the house was empty because the amount of data being sent out slowed, but her home remained active despite being empty. All of her devices, from her TV to her WeMo smart plugs, continued to send out information every day. But the Behmor Connected coffee machine seemed to miss its inhabitants because it completely freaked out. The coffee machine, which typically pings its servers a few times a day, phoned home over 2,000 times on Thursday, January 24th.

A comparison in data:

When we asked Behmor about it, they had to check with Dado Labs, the third-party that makes their devices smart. "We pretty much stay at arm's length. We don't even have our customers' email addresses for log-in," said Joe Behm from Behmor. Dado Labs reported that a server was down that day, meaning the coffee machine just kept calling and calling a line that wouldn't pick up.

Overall, my takeaway is that the smart home is going to create a new stream of information about our daily lives that will be used to further profile and target us. The number of devices alone that are detected chattering away will be used to determine our socioeconomic status. Our homes could become like internet browsers, with unique digital fingerprints, that will be mined for profit just like our daily Web surfing is. If you have a smart home, it's open house on your data.

I was looking forward to the end of the experiment and getting rid of all the internet-connected devices I'd accumulated, as well as freeing up the many electrical outlets they'd been hogging. The Internet of Shit Twitter account is right. Smart homes are dumb.

But the truth is that my house will remain smart, just like yours may be. Almost every TV on the market now is connected-because otherwise how do you Netflix and chill?-and over 25 million smart speakers were sold last year alone, with Apple soon to release its version, the HomePod, meaning a good percentage of American homes have or will have an internet-connected assistant waiting patiently for someone in the house to say their wake word.

In fact, the most disturbing product in my home was in it before the experiment even started: the Vizio Smart TV. Beyond finding out that my TV was letting data brokers know what Hulu shows we watched, the experiment led me to actually visit Vizio's privacy policy, which was full of horrifying language about the TV's ability to collect second-by-second information about everything we watched-from shows to video streams to DVDs to commercials-and sell it to advertisers, who could then track our activity on other devices on the same IP address to see whether we went to a particular website because of what we saw on TV.

This ability was turned on by default for the 11 million smart TVs Vizio had sold to consumers since 2014. Our TV had likely been tracking everything we watched until 2017, when the Federal Trade Commission and the New Jersey Attorney General sued the company for unfair and deceptive practices. Vizio wound up settling the suit for $2.2 million and turning off the tracking unless the TV owners chose to turn it on (in the pursuit of well-targeted ads?).

We may already be past the point of no return: internet functionality is a necessary component for the operation of many devices in our home, and it increasingly gets added on as a feature even when it's not strictly necessary. So when you get a sex toy, it connects to the internet just in case your partner wants to be able to please you from afar. But once the data is going over the wires, companies can't seem to resist peeking at it, no matter how sensitive it is. The Canadian company We-Vibe wound up paying millions of dollars to its customers because its internet-connected sex toy was collecting stats about their orgasms, for "market research. "

What our experiment told us is that all the connected devices constantly phone home to their manufacturers. You won't be aware these conversations are happening unless you're technically savvy and monitoring your router like we did. And even if you are, because the conversations are usually encrypted, you won't be able to see what your belongings are saying. When you buy a smart device, it doesn't just belong to you; you share custody with the company that made it.

When you buy a smart device, it doesn't just belong to you; you share custody with the company that made it.

That's not just a privacy concern. It also means that those companies can change the product you bought after you buy it. So your smart speaker can suddenly become the hub of a social network, and your fancy smart scale can have one of its key features taken away in a firmware update.

There was one connected device I came to love as a result of the experiment: the iRobot Roomba 890 vacuum. (It doesn't make a map of my home, by the way, because it's not the newest 900 series.) It terrified my daughter, who cried and ran to me every time the low, round robot rolled in as if the clown from Stephen King's It had just entered the room, but I loved it. The Roomba did what robots do best: easy, boring, monotonous work. But it didn't do so independently; like my other "smart" products, it used its internet connectivity to pop up notifications on my phone. Being smart meant it could nag me:

"Roomba requires your attention: Your Roomba is stuck."

"Roomba requires your attention: Your Roomba's bin is full."

"Roomba's cleaning job was canceled."

I thought the house would take care of me but instead everything in it now had the power to ask me to do things. Ultimately, I'm not going to warn you against making everything in your home smart because of the privacy risks, although there are quite a few. I'm going to warn you against a smart home because living in it is annoying as hell.
Data analysis contributed by Dhruv Mehrotra This story was produced with support from the Mozilla Foundation as part of its mission to educate individuals about their security and privacy on the internet.

# data[].attributes.attachment_link.description
In December, I converted my one-bedroom apartment in San Francisco into a "smart home." I connected as many of my appliances and belongings as I could to the internet: an Amazon Echo, my lights, my coffee maker, my baby monitor, my kid's toys, my vacuum, my TV, my toothbrush, a photo frame, a sex toy, and even my bed.

# data[].attributes.body
Here's the instrument (the theremin)that Project Soli reminded me of.

# data[].attributes.body
Recommended for Surveillance Capitalism --> "About Us, Not For Us"

# data[].attributes.attachment_link.readable_text
What fans say about the podcast

Always interesting

This is by far my favorite data podcast. I've listened to a bunch of different podcasts and I find RAW Data to be by far the best mix of expectation of understanding while still informational about interesting topics. I hope it becomes a weekly podcast!

- Lifer8

My favorite podcast

Succinct, informative, thought provoking

- -.-"""""

Radio Lab for the Information Age

The 4th season has a makeover: captivating, fascinating, and totally unique. Stories I never would have thought about and make me see the world in a new light.

- paulfarmer1

Always interesting

This is by far my favorite data podcast. I've listened to a bunch of different podcasts and I find RAW Data to be by far the best mix of expectation of understanding while still informational about interesting topics. I hope it becomes a weekly podcast!

- Lifer8

My favorite podcast

Succinct, informative, thought provoking

- -.-"""""

Radio Lab for the Information Age

The 4th season has a makeover: captivating, fascinating, and totally unique. Stories I never would have thought about and make me see the world in a new light.

- paulfarmer1

Mike Osborne

Executive Producer & Host

Mike began his career as a research scientist. He started his first podcast while working toward his PhD in environmental sciences at Stanford. After earning his doctorate, he went to work for Stanford as a radio and podcast producer. He co-created Raw Data in 2015.

Andrea Mustain

Executive Producer & Host

Andrea is a producer, writer, and editor specializing in science, business, and technology. She has produced pieces for PBS, PRX, and Marketplace, among others. Her written work appears in publications from Scientific American to the Christian Science Monitor. She has also spent time working in the tech industry itself. She has a B.S. from Northwestern University, and an M.S. from the Columbia Graduate School of Journalism. She lives in Brooklyn, and dreams that maybe, just maybe, she can someday live in a place with a real kitchen counter.

The Raw Data Team

These people make us sound good...

Ian Coss is our sound designer, and Curtis Fox is our editor. Michael Linder of HatPineapple made our theme, and some of our incidental music. John Barth, Genevieve Sponsler, and Ray Pang provide additional support from PRX.

The Answer Machine

Google has become our yellow pages, our atlas, our library, our medical consultant, our shopping guide. Which means it is, basically, a giant, ...

About Us, But Not for Us

Shoshana Zuboff doesn't mince words when it comes to the data economy. According to Zuboff, author of the recent book *The Age of Surveillance ...

The United States is a pretty divided country; which may just feel like an inevitable product of our times. But it turns out there's one partisan ...

View all episodes

A podcast about how information becomes power

# data[].attributes.attachment_link.description
We've entered a new era. The creation and collection of information play an ever-increasing - yet often hidden - role in our lives. Algorithms filter all sorts of experiences, from the mundane to the monumental. The fuel that powers and curates these experiences is...data. Data are the new oil; whoever controls data has power.

# data[].attributes.body
Boston Dynamics released their robot dog called Spot. The dog can do whatever the user programs it to do so it has versatility. Spot can do construction work, navigation, and entertainment.
https://www.bostondynamics.com/spot

# data[].attributes.body
Project Soli is developing a new interaction sensor using radar technology. The sensor can track sub-millimeter motions at high speed and accuracy. The plan is for it to fit onto a chip, and can be produced at scale and built into small devices and everyday objects.

# data[].attributes.body
1/28

# data[].attributes.attachment_link.title
How job surveillance is changing trucking in America

# data[].attributes.attachment_link.readable_text
When Anne Sullivan came to Tewksbury, she wasn't yet the renowned "miracle worker" who would teach Helen Keller to communicate. It was 1876, and 10-year-old Annie was a blind child living in abject poverty. Her years at the poorhouse-a facility designed to house poor people in a time before social services- were "a crime against childhood," she later remembered.

Residents at the Massachusetts poorhouse milled about like forgotten animals. As Anne and her brother slept on the institution's iron cots in a gigantic dormitory, rats ran up and down the spaces between beds.

In 1883, a massive investigation exposed the conditions at Tewksbury-but the institution was far from unique. Throughout the 19th and early 20th centuries, poorhouses were a reality for society's most vulnerable people. These locally run institutions filled a need in a time before Social Security, Medicaid and Section 8 housing became a reality. They also exposed the stigma and shame society placed on those who were unable to support themselves.

The concept of the poorhouse originated in England during the 17th century. Municipalities were expected to care for their poor, and made a distinction between people who were old and unable to care for themselves and the able-bodied. People who were able to work were expected to do so-and could be imprisoned if they refused.

They lived in workhouses, bare bones facilities designed to make poverty seem even less attractive. In these facilities, poor people ate thrifty, unpalatable food, slept in crowded, often unsanitary conditions, and were put to work breaking stones, crushing bones, spinning cloth or doing domestic labor, among other jobs.

In the United States, the idea emigrated along with English colonists. In 1660, Boston built its first workhouse -a brick building intended for "dissolute and vagrant persons." Massachusetts' poor people had more than the workhouse to fear: Towns could also banish poor people or even auction them off to the lowest bidder. "Warning out" allowed towns to exile poor newcomers or make it clear they were not willing to pay to support them.

The vendue system allowed cities to auction off poor individuals to private bidders. The individual who bought the poor person then put them to work in exchange for reimbursement of what it cost to clothe and feed them. Sometimes, people had another option-asking the Overseer of the Poor, a town official, for relief. In some cases, the overseer would provide them with town-sponsored food, clothing or firewood.

By the early 19th century, the poorhouse system had won out over warning or vendue-and their construction coincided with an increasingly negative attitude toward poor people. These facilities were designed to punish people for their poverty and, hypothetically, make being poor so horrible that people would continue to work at all costs. Being poor began to carry an intense social stigma, and increasingly, poorhouses were placed outside of public view.

Paupers struck a difficult bargain in exchange for shelter and food. As historian Debbie Mauldin Cottrell writes, many states required them to give an oath "swearing to their lack of worldly goods and to their need for assistance," a ritual designed to weed out those who didn't want to publicly swear that they were poor. Once they were an "inmate" of the facility, they had to submit to an often draconian control of what they ate and wore and how they worked and acted. "Consequently, it was the most desperate, those with the least pride, who often populated poor facilities," Cottrell notes.

Often, conditions in poorhouses were alarming. However, life in the poorhouse was not always miserable. Historians have documented the ways poor people used workhouses and poor farms as places in which to build community during their most vulnerable moments. As historian Ruth Wallis Herndon has noted, many women returned to the Boston Almshouse again and again and maintained connections to the outside world while inside the poorhouse. "For most men, on the other hand, the Almshouse was an unfamiliar place in an unfamiliar city," she writes.

For men on the move, there were alternatives to poorhouses: the tramp house. These tiny, temporary homes were erected for vagrants and itinerant people-often men-passing through communities. Often amounting to little more than shacks, these houses provided the bare basics, like mattresses and firewood, to people in towns near railroads, and generally weren't publicized since communities didn't want to advertise their charity toward vagrants.

As society tried to hide institutions designed to help the poor, poorhouses took on another form: the poor farm. Like poorhouses, these institutions were carefully regulated places for indigent people to live and work. However, poor farms were located in rural areas and the outskirts of cities instead of city centers. Instead of performing industrial or domestic labor, residents did farm work instead.

The poorhouse faded out around the time of the Great Depression as the federal government became more involved with social welfare. Most remaining poor farms and poorhouses closed in the 1930s and 1940s, though a few remained in places like Texas until the 1970s.

Though the poorhouses are no longer, their memory is preserved in testimony by people like Anne Sullivan. "I doubt if life, or eternity for that matter, is long enough to erase the errors and ugly blots scored upon my brain by those dismal years," she wrote later.

Fact Check

We strive for accuracy and fairness. But if you see something that doesn't look right, click here to contact us! HISTORY reviews and updates its content regularly to ensure it is complete and accurate.

# data[].attributes.attachment_link.title
Poorhouses Were Designed to Punish People for Their Poverty | HISTORY

# data[].attributes.attachment_link.description
In a time before social services, society's most vulnerable people were hidden away in brutal institutions.

# data[].attributes.body
This article discusses how protestors in Hong Kong are utilizing tech to their advantage; even to counter the police's use of technology. For example, protestors have used laser pointers to evade China's facial recognition tech.

Here's another article on how protestors are using day-use
transport passes rather than metro cards, to avoid location
tracking and leaving a paper trail:

https://gizmodo.com/whathong-kongs-protestors-can-teach-us-about-the-futur1835715794

"Privacy harms are often a time-shifted risk. In a world that runs on data, everyday uses of technology can suddenly put people in
danger when circumstances change. This also means the opposite: Most of the time, most people won’t feel the cost of having their data exploited."

# data[].attributes.attachment_link.readable_text
A police station is pointed at with laser beams by protesters in Hong Kong [File: Tyrone Siu/Reuters]

With thousands taking part in Hong Kong's pro-democracy protests, technology has come to play an important role in the demonstrations.

Authorities have been using technology to their advantage, prompting the protesters to counter that with moves of their own.

Al Jazeera looks at three examples of how the use of technology, from mobile phones to facial recognition, has become a crucial element in the ongoing protests in Hong Kong.

Countering facial recognition

During the early days of the protests, a video showing dozens of laser pointers aimed at the police went viral.

The protesters were using the laser pointers to not only disorientate the police, but also to circumvent China's mass surveillance systems, particularly the facial recognition systems.

Beijing's facial recognition capabilities first came to global attention when China's detention of more than one million Uighurs in its western Xinjiang province made headlines.

Among other things, China's use of facial recognition software enabled it to have a tight control over the Uighur population.

Hong Kong protestors are on another level. Here they're using lasers to avoid facial recognition cameras. A cyber war against Chinese artificial intelligence. pic.twitter.com/t1hIczr5Go
- Alessandra (@alessabocchi) 31 July 2019

The police have warned the protesters against using these types of lasers, saying that they could blind the officers.

However, the protesters have not stopped using them despite those calls.

Other more low-tech solutions by the protesters have been covering their faces with masks and using umbrellas to prevent the cameras from filming and identifying them.

Pro-China Twitter accounts

The scuffles have been taking place on the streets of Hong Kong. But the mood has been echoed online, especially on Twitter which has been used extensively by followers of both sides.

Twitter has been the means of getting the word out to the world for many protesters, ensuring their voices are heard by an international audience.

However, as the protests progressed, a steady stream of pro-China accounts slowly emerged, with thousands echoing talking points in support of Beijing.

The accounts, which often have a combination of a randomly generated set of letters as user names, had been dormant for years. But now, with protests in Hong Kong gaining significant traction on Twitter, these accounts have come alive.

Often, they will only have a couple of tweets, hardly any followers and follow only a few accounts. But these accounts are sending out messages in favour of the Chinese government.

According to research done by Al Jazeera for previous articles about state-backed propaganda on Twitter, these are all common traits for Twitter bots and trolls.

The Chinese government has blocked access to Twitter in mainland China, which means that people behind these accounts could not realistically be in China unless they went through several steps to circumvent Beijing's censorship.

On top of that, circumventing the so-called Great Firewall is illegal for anyone in mainland China, so even people tweeting messages in support of the Chinese government would risk repercussions.

Snooping on travellers' phones

On Thursday, Bloomberg reported that several people travelling to China from Hong Kong were asked by Chinese border security personnel to unlock their phones and share their activities.

If photographs of the protests were present on the phones, more information was written down by border security.

Another report from the South China Morning Post (SCMP) noted similar practices, with one person quoted as saying a book about dating in modern China was confiscated.

"They first went through my photos and videos, one by one, and asked me to explain what was happening," an SCMP journalist was quoted as saying.

Because of these stringent measures, many decided to either delete certain apps and information from their phones, or buy new ones before travelling to China.

# data[].attributes.attachment_link.title
Laser beams, Twitter war: The tech side of Hong Kong protests

# data[].attributes.attachment_link.description
With thousands taking part in Hong Kong's pro-democracy protests, technology has come to play an important role in the demonstrations. Authorities have been using technology to their advantage, prompting the protesters to counter that with moves of their own.