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Technological Repression of Xinjiang Muslims 

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Technological Repression of Xinjiang Muslims 

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Human test subjects – Nazi Medical Experiments 

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Human test subjects – Nazi Medical Experiments 

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Satellite Technologies in Emergency Response 

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Satellite Technologies in Emergency Response 

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Chips monitoring Chinese Students (ABC, 2018)

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Chips monitoring Chinese Students (ABC, 2018)

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Summary:
Chinese schools have begun enforcing "smart uniforms" embedded with computer chips to monitor student movements and prevent them from skipping classes. Eleven schools in the south-west province of Guizhou have introduced the uniforms, which were developed by local tech firm Guizhou Guanyu Technology. As students enter the school, the time and date is recorded along with a short video that parents can access via a mobile app. Facial recognition further ensures that each uniform is worn by its rightful owner to prevent students from cheating the system.

Key points:

Skipping class or leaving the classroom without permission triggers an alarm

GPS system can track student movements even beyond the school grounds

Beijing has made regular calls on all schools to develop "smart campuses"

Pros:
- Convenience for school/teachers- attendance taking etc.
- Safety (especially Covid situation: contact tracing)
- Gives parents the peace of mind knowing the whereabouts of their children

Cons & Limitations:
- Loss of freedom for students (restricted movements)
- Lack of privacy
- Data leak / possible of hacking?-  is the data/network secure?
- Cost/pricing: how much and who is to bear for the cost?

Possible Controversial issues:

- Security at the cost of privacy
- Needs vs Desires (from whose perspective?)
- Freedom/Independence vs State/institutional control

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Chinese schools have begun enforcing "smart uniforms" embedded with computer chips to monitor student movements and prevent them from skipping classes.

Skipping class or leaving the classroom without permission triggers an alarm

A GPS system tracks student movements even beyond the school grounds

Beijing has made regular calls on all schools to develop "smart campuses"

Eleven schools in the south-west province of Guizhou have introduced the uniforms, which were developed by local tech firm Guizhou Guanyu Technology.

As students enter the school, the time and date is recorded along with a short video that parents can access via a mobile app.

Facial recognition further ensures that each uniform is worn by its rightful owner to prevent students from cheating the system.

Skipping classes triggers an alarm to inform teachers and parents of the truancy, while an automatic voice alarm activates if a student walks out of school without permission.

A GPS system tracks student movements even beyond the school grounds.

The two chips - inserted into each uniform's shoulders - can withstand up to 500 washes and 150 degrees Celsius, the company told state media Global Times.

Alarms will also sound if a student falls asleep in class, while parents can monitor purchases their child makes at the school and set spending limits via a mobile app, according to the company's official website.

The company released a public statement via popular Chinese social media site Weibo saying the uniforms "focus on safety issues", and provide a "smart management method" that benefits students, teachers and parents.

Guanyu Technology's marketing manager also bragged of the uniforms' capabilities on a personal Weibo account.

"You go to any school and ask the security guard how many students there are in the school today. He definitely can't give you an answer, but we can," the post read.

On its website, the development company said the uniforms were designed to "fully implement the state policy of actively constructing smart campuses and smart education management for the development of education".

In recent years, Beijing has made regular calls on all schools to develop "smart campuses" in a push to digitalise education.

'Don't children have human rights and privacy?'

A GPS system tracks students' movements even beyond the school grounds.(Supplied: Guanyun Technology )

Despite all the benefits touted by the company, the uniforms have prompted backlash on social media.

"If you were a child, would you like to be monitored 24 hours a day?" asked one Weibo user with the nickname Sangguo_Sunny.

"Don't children have human rights and privacy?" user Dawson-HO said.

But blogger Yi Zhi Sirius defended the new uniforms, saying "the uniforms will come in handy when it comes to locating missing students".

Guanyu Technology's marketing manager also bragged of the uniforms' capabilities on a personal Weibo account.(Supplied: Guanyu Technology )

Speaking to the Global Times, Lin Zongwu, principal of Number 11 School of Renhuai in Guizhou, said although the school had the ability to track students at all times, they used this technology sparingly.

"We choose not to check the accurate location of students after school, but when the student is missing and skipping classes, the uniforms help locate them," Mr Lin said, adding the attendance rate had increased since the intelligent uniforms were introduced.

Guanyu Technology responded to criticism on Weibo, saying the company did "respect and protect human rights".

"The smart uniform does not track students' every single move all the time," the company wrote.

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Chinese schools enforce 'smart uniforms' with GPS tracking system to monitor students

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Chinese schools have begun enforcing "smart uniforms" embedded with computer chips to monitor student movements and prevent them from skipping classes. Skipping class or leaving the classroom without permission triggers an alarm A GPS system tracks student movements even beyond the school grounds Beijing has made regular calls on all schools to develop "smart campuses" Eleven schools in the south-west province of Guizhou have introduced the uniforms, which were developed by local tech firm Guizhou Guanyu Technology.

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China’s Social Credit System (South China Morning Post, 2020)

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China’s Social Credit System (South China Morning Post, 2020)

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Merits for the Chinese government to implement this policy/system:
- To set and direct ("police"?) moral conduct/behaviour of society
- To transform / "control" social behaviour (especially for rural villages or even the cities- depending on how one sees it. For rural villages, the need for improvements as it is still relatively backward and slow whereas for cities, need to impose greater social control with possibly increased calls for freedom and independence/autonomy from the state. )
- Social accountability (everyone's behaviour/actions or the lack thereof impacts on another)

Possible criticisms of this system:
- Based system (people/ the information collectors may not make fair/ subjective judgments)- may lead to nepotism or bribery
- Also consider: Would technology definitely/necessarily do a better job?
- People's good behaviour to earn credits is not genuine: other motivations (selfish/ self-interests at hand) as they benefit as well.
- may not be fair to expect everyone, especially the elderly (seen in the example: 80 years old still has to provide labour) to contribute to this social credit system.

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national development and reform commission (ndrc)

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how does china’s social credit system work?

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why has china set up a social credit system?

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what is the progress of china’s social credit system

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why is china’s social credit system controversial?

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What is China's social credit system and why is it controversial?

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China's social credit system is a set of databases and initiatives that monitor and assess the trustworthiness of individuals, companies and government entities.

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Why Algorithms can be Racist and Sexist (Vox, 2020)

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Why Algorithms can be Racist and Sexist (Vox, 2020)

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Summary:
Algorithmic bias can be seen in two primary ways: accuracy and impact. An AI can have different accuracy rates for different demographic groups. Similarly, an algorithm can make vastly different decisions when applied to different populations.

Importantly, when you think of data, you might think of formal studies in which demographics and representation are carefully considered, limitations are weighed, and then the results are peer-reviewed. That’s not necessarily the case with the AI-based systems that might be used to make a decision about you. Let’s take one source of data everyone has access to: the internet. One study found that, by teaching an artificial intelligence to crawl through the internet — and just reading what humans have already written — the system would produce prejudices against black people and women. Another example of how training data can produce sexism in an algorithm occurred a few years ago, when Amazon tried to use AI to build a résumé-screening tool. According to Reuters, the company’s hope was that technology could make the process of sorting through job applications more efficient. It built a screening algorithm using résumés the company had collected for a decade, but those résumés tended to come from men. That meant the system, in the end, learned to discriminate against women. It also ended up factoring in proxies for gender, like whether an applicant went to a women’s college. (Amazon says the tool was never used and that it was nonfunctional for several reasons.)

*Food for thought: Given that it is known that there is bias in algorithms, why do we (instituitions, companies, governments etc.) still continue to use (and rely on) them?

Just because a technology is accurate doesn’t make it fair or ethical. For instance, the Chinese government has used artificial intelligence to track and racially profile its largely Muslim Uighur minority, about 1 million of whom are believed to be living in internment camps.

Relevant controversial issues:

Also consider Intersectionality  (intersecting and overlapping social identities- which could prove to be empowering for some, yet oppressing for others)

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This story is part of a group of stories called

Uncovering and explaining how our digital world is changing - and changing us.

Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire.

But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. It's tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box. We frequently don't know how a particular artificial intelligence or algorithm was designed, what data helped build it, or how it works.

Typically, you only know the end result: how it has affected you, if you're even aware that AI or an algorithm was used in the first place. Did you get the job? Did you see that Donald Trump ad on your Facebook timeline? Did a facial recognition system identify you? That makes addressing the biases of artificial intelligence tricky, but even more important to understand.

Machine learning-based systems are trained on data. Lots of it.

When thinking about "machine learning" tools (machine learning is a type of artificial intelligence), it's better to think about the idea of "training." This involves exposing a computer to a bunch of data - any kind of data - and then that computer learns to make judgments, or predictions, about the information it processes based on the patterns it notices.

For instance, in a verysimplified example, let's say you wanted to train your computer system to recognize whether an object is a book, based on a few factors, like its texture, weight, and dimensions. A human might be able to do this, but a computer could do it more quickly.

To train the system, you show the computer metrics attributed to a lot of different objects. You give the computer system the metrics for every object, and tell the computer when the objects are books and when they're not. After continuously testing and refining, the system is supposed to learn what indicates a book and, hopefully, be able to predict in the future whether an object is a book, depending on those metrics, without human assistance.

That sounds relatively straightforward. And it might be, if your first batch of data was classified correctly and included a good range of metrics featuring lots of different types of books. However, these systems are often applied to situations that have much more serious consequences than this task, and in scenarios where there isn't necessarily an "objective" answer. Often, the data on which many of these decision-making systems are trained or checked are often not complete, balanced, or selected appropriately, and that can be a major source of - although certainly not the only source - of algorithmic bias.

Nicol Turner-Lee, a Center for Technology Innovation fellow at the Brookings Institution think tank, explains that we can think about algorithmic bias in two primary ways: accuracy and impact. An AI can have different accuracy rates for different demographic groups. Similarly, an algorithm can make vastly different decisions when applied to different populations.

Importantly, when you think of data, you might think of formal studies in which demographics and representation are carefully considered, limitations are weighed, and then the results are peer-reviewed. That's not necessarily the case with the AI-based systems that might be used to make a decision about you. Let's take one source of data everyone has access to: the internet. One study found that, by teaching an artificial intelligence to crawl through the internet - and just reading what humans have already written - the system would produce prejudices against black people and women.

Another example of how training data can produce sexism in an algorithm occurred a few years ago, when Amazon tried to use AI to build a résumé-screening tool. According to Reuters, the company's hope was that technology could make the process of sorting through job applications more efficient. It built a screening algorithm using résumés the company had collected for a decade, but those résumés tended to come from men. That meant the system, in the end, learned to discriminate against women. It also ended up factoring in proxies for gender, like whether an applicant went to a women's college. (Amazon says the tool was never used and that it was nonfunctional for several reasons.)

Amid discussions of algorithmic biases, companies using AI might say they're taking precautions, taking steps to use more representative training data and regularly auditing their systems for unintended bias and disparate impact against certain groups. But Lily Hu, a doctoral candidate at Harvard in applied mathematics and philosophy who studies AI fairness, says those aren't assurances that your system will perform fairly in the future.

"You don't have any guarantees because your algorithm performs 'fairly' on your old dataset," Hu told Recode. "That's just a fundamental problem of machine learning. Machine learning works on old data [and] on training data. And it doesn't work on new data, because we haven't collected that data yet."

Still, shouldn't we just make more representative datasets? That might be part of the solution, though it's worth noting that not all efforts aimed at building better data sets are ethical. And it's not just about the data. As Karen Hao of the MIT Tech Review explains, AI could also be designed to frame a problem in a fundamentally problematic way. For instance, an algorithm designed to determine "creditworthiness" that's programmed to maximize profit could ultimately decide to give out predatory, subprime loans.

Here's another thing to keep in mind: Just because a tool is tested for bias - which assumes that engineers who are checking for bias actually understand how bias manifests and operates - against one group doesn't mean it is tested for bias against another type of group. This is also true when an algorithm is considering several types of identity factors at the same time: A tool may deemed fairly accurate on white women, for instance, but that doesn't necessarily mean it works with black women.

In some cases, it might be impossible to find training data free of bias. Take historical data produced by the United States criminal justice system. It's hard to imagine that data produced by an institution rife with systemic racism could be used to build out an effective and fair tool. As researchers at New York University and the AI Now Institute outline, predictive policing tools can be fed "dirty data," including policing patterns that reflect police departments' conscious and implicit biases, as well as police corruption.

The foundational assumptions of engineers can also be biased

So you might have the data to build an algorithm. But who designs it, and who decides how it's deployed? Who gets to decide what level of accuracy and inaccuracy for different groups is acceptable? Who gets to decide which applications of AI are ethical and which aren't?

While there isn't a wide range of studies on the demographics of the artificial intelligence field, we do know that AI tends to be dominated by men. And the "high tech" sector, more broadly, tends to overrepresent white people and underrepresent black and Latinx people, according to the Equal Employment Opportunity Commission.

Turner-Lee emphasizes that we need to think about who gets a seat at the table when these systems are proposed, since those people ultimately shape the discussion about ethical deployments of their technology.

But there's also a broader question of what questions artificial intelligence can help us answer. Hu, the Harvard researcher, argues that for many systems, the question of building a "fair" system is essentially nonsensical, because those systems try to answer social questions that don't necessarily have an objective answer. For instance, Hu says algorithms that claim to predict a person's recidivism don't ultimately address the ethical question of whether someone deserves parole.

"There's not an objective way to answer that question," Hu says. "When you then insert an AI system, an algorithmic system, [or] a computer, that doesn't change the fundamental context of the problem, which is that the problem has no objective answer. It's fundamentally a question of what our values are, and what the purpose of the criminal justice system is."

That in mind, some algorithms probably shouldn't exist, or at least they shouldn't come with such a high risk of abuse. Just because a technology is accurate doesn't make it fair or ethical. For instance, the Chinese government has used artificial intelligence to track and racially profile its largely Muslim Uighur minority, about 1 million of whom are believed to be living in internment camps.

Transparency is a first step for accountability

One of the reasons algorithmic bias can seem so opaque is because, on our own, we usually can't tell when it's happening (or if an algorithm is even in the mix). That was one of the reasons why the controversy over a husband and wife who both applied for an Apple Card - and got widely different credit limits - attracted so much attention, Turner-Lee says. It was a rare instance in which two people, who at least appeared to be exposed to the same algorithm and could easily compare notes. The details of this case still aren't clear, though the company's credit card is now being investigated by regulators.

But consumers being able to make apples-to-apples comparisons of algorithmic results are rare, and that's part of why advocates are demanding more transparency about how systems work and their accuracy. Ultimately, it's probably not a problem we can solve on the individual level. Even if we do understand that algorithms can be biased, that doesn't mean companies will be forthright in allowing outsiders to study their artificial intelligence. That's created a challenge for those pushing for more equitable, technological systems. How can you critique an algorithm - a sort of black box - if you don't have true access to its inner workings or the capacity to test a good number of its decisions?

Companies will claim to be accurate, overall, but won't always reveal their training data (remember, that's the data that the artificial intelligence trains on before evaluating newdata, like, say, your job application). Many don't appear to be subjecting themselves to audit by a third-party evaluator or publicly sharing how their systems fare when applied to different demographic groups. Some researchers, such as Joy Buolamwini and Timnit Gebru, say that sharing this demographic information about both the data used to train and the data used to check artificial intelligence should be a baseline definition of transparency.

Artificial intelligence is new, but that doesn't mean existing laws don't apply

We will likely need new laws to regulate artificial intelligence, and some lawmakers are catching up on the issue. There's a bill that would force companies to check their AI systems for bias through the Federal Trade Commission (FTC). And legislation has also been proposed to regulate facial recognition, and even to ban the technology from federally assisted public housing.

But Turner-Lee emphasizes that new legislation doesn't mean existing laws or agencies don't have the power to look over these tools, even if there's some uncertainty. For instance, the FTC oversees deceptive acts and practices, which could give the agency authority over some AI-based tools.

The Equal Employment Opportunity Commission, which investigates employment discrimination, is reportedly looking into at least two cases involving algorithmic discrimination. At the same time, the White House is encouraging federal agencies that are figuring out how to regulate artificial intelligence to keep technological innovation in mind. That raises the challenge of whether the government is prepared to study and govern this technology, and figure out how existing laws apply.

"You have a group of people that really understand it very well, and that would be technologists," Turner-Lee cautions, "and a group of people who don't really understand it at all, or have minimal understanding, and that would be policymakers."
Open Sourced is made possible by Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.
That's not to say there aren't technical efforts to "de-bias" flawed artificial intelligence, but it's important to keep in mind that the technology won't be a solution to fundamental challenges of fairness and discrimination. And, as the examples we've gone through indicate, there's no guarantee companies building or using this tech will make sure it's not discriminatory, especially without a legal mandate to do so. It would seem it's up to us, collectively, to push the government to rein in the tech and to make sure it helps us more than it might already be harming us.

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Uncovering and explaining how our digital world is changing - and changing us. Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire.

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Nazi experimentation with twins (Hannah and Shaine)

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Nazi experimentation with twins (Hannah and Shaine)

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The main subjects that they used for their experiments during the holocaust were Jews, especially Jewish twins. This was because twins are biologically similar so one would be used as a control while the other one is subjected to torturous acts that were guised as “experiments”. Some of these torturous acts included forced inseminations and amputation. These experiments would ultimately kill the twins. Scientists reasoned that any behavioural differences were not due to their genetics. However, eugenicists held genetics responsible for any undesired traits and social conditions like poverty and criminality. Hence eugenicists turned to selective breeding to encourage socially acceptable behaviour and wipe out undesirable tendencies, but to no avail.

Controversies/ethical perspectives/tensions:
- goes against human rights and involves the discrimination of Jews
- selective breeding of humans can go against human rights as well
-

Solutions:
- increased awareness of the issues brought about by discrimination (tackling root cause)
- now there are new technologies that can substitute the use of humans as subjects for experiments that can endanger their lives such as ‘in vitro testing’ and computer modelling.
- if human subjects are required, they have to provide their consent so that they are not being exploited.

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Why the Nazis Were Obsessed With Twins | HISTORY

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Horrifying medical experiments on twins helped Nazis justify the Holocaust.

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Biometric Registration of Rohingya Refugees 

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Biometric Registration of Rohingya Refugees 

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Clinical Trials for Vaccine Development 

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Clinical Trials for Vaccine Development 

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Electronic monitoring bracelets (The Conversation, 2019)

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Electronic monitoring bracelets (The Conversation, 2019)

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Summary:
The rise of electronic monitoring- here it is used in the corrections systems and it refers to the tagging of a person as a form of surveillance, usually in the form of a GPS-enabled ankle bracelet. When an offender is subject to electronic monitoring, a computer database is updated with information about the rules he or she has been instructed to follow. Each jurisdiction and each agency may have their own database, so where the offender appears in the database will depend on who is supervising the electronic monitoring order. The database is then monitored by enforcement authorities, although this is sometimes outsourced to private providers or overseas companies. While the data is generally sent from the offender’s GPS device to the monitoring agency in real time, there can be delays in how long it takes for that information to be passed to police or corrective services.

Pros:
- whereabouts could be tracked
- reduce manpower and alleviate the burden on prisons
- increased security
- deterrence of crime

Cons & Limitations:
- would this platform be more expensive? (cost in terms of the technology/equipment vs cost in terms of recidivism)
- Does it solve the root cause/issues of crime/why potential offenders commit crime?
- Possible source of discrimination- easily identified by others
- May not be as secure (cases where such items have been removed)
- Health issues relating to these items (?)

Possible Controversial Issues:

Consider other possible contexts where such electronic monitoring could be employed and the possible controversies/tensions such use could pose:
- Contact tracing (for Covid) - balance between care and privacy
- State control for example China- social credit system)

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The man arrested after a deadly gun attack in Darwin Tuesday night is reported to have been on parole and wearing an electronic monitoring bracelet.

This leads to the same reaction we see following any high-profile crime. How could such a thing happen?

People may speculate that the criminal justice agencies involved have somehow dropped the ball. The offender was on their radar, after all.

Read more: Online tools can help people in disasters, but do they represent everyone?

While this finger-pointing may serve a cathartic function, it is important we also question our expectations before assuming a failure occurred.

We need to understand what electronic monitoring intends to achieve, how it works, and what are its capabilities and limitations.

Electronic tagging

In the context of the corrections system, electronic monitoring refers to the tagging of a person as a form of surveillance, usually in the form of a GPS-enabled ankle bracelet.

In Australia, each state and territory uses electronic monitoring differently, guided by their own legislative frameworks.

Practices vary considerably between jurisdictions. For example, in some places, certain offenders are targeted (high-risk recidivists, those who repeatedly reoffend, for example). In others, specific types of offences are the focus (such as child sex offences).

The application of electronic monitoring even differs between offenders, as the supervising agency uses it for reasons specific to each person.

A police department might use electronic monitoring to ensure a domestic violence perpetrator does not visit the victim before a trial. A probation officer might require an offender to wear a bracelet for 12 months to ensure they are attending treatment and meeting their curfew. A parole officer could place the GPS tracking condition on an offender for the first three months following release from prison to better understand how the parolee spends his or her time.

Each of these experiences will be quite different, as each is intended to fulfil a unique aim.

Ordinarily, electronic monitoring is used as a tool of incapacitation and deterrence.

In the first instance, an offender may be told to follow a particular rule - for example, to be home by 8pm, to stay away from the victim, to attend a treatment program, or not to go within 1km of a school. Electronic monitoring allows authorities to monitor the person's compliance with such a condition.

In the latter instance, an offender may be deterred from certain behaviour if they believe their actions are likely to be detected through electronic monitoring.

Monitoring actions

When an offender is subject to electronic monitoring, a computer database is updated with information about the rules he or she has been instructed to follow. Each jurisdiction and each agency may have their own database, so where the offender appears in the database will depend on who is supervising the electronic monitoring order.

The database is then monitored by enforcement authorities, although this is sometimes outsourced to private providers or overseas companies. While the data is generally sent from the offender's GPS device to the monitoring agency in real time, there can be delays in how long it takes for that information to be passed to police or corrective services.

What occurs when an offender breaches one of the rules and a computer alert is generated depends on factors such as legislation and the priority of a case influencing the response. The database includes information about what to do in the event of specific kinds of breaches with specific offenders.

In some cases, an alarm on the device may go off or, very rarely, the police may be immediately notified.

Most often, for routine cases and ordinary breaches, the monitoring agency will notify the offender's supervisor (such as a parole officer or a local police department), who will then determine how to proceed.

There may be a lag of several days during this process. For example, if a low-risk offender misses their home curfew on Friday night (as determined by the GPS bracelet), the parole officer will not receive notification of this breach until Monday morning.

The pros and cons of tagging

There are a range of benefits and disadvantages to the electronic monitoring of offenders.

It can be effective in holding offenders accountable, protecting victims and enhancing community safety and preventing crimes. These come with important cost savings, particularly when offenders can be safety monitored in the community in lieu of imprisonment or as a mechanism of early release from prison.

But some of the downfalls are that offenders can tamper with their devices, and there can be GPS dead zones - particularly in a geographically vast country such as Australia. There may also be human error in using the systems, such as improper monitoring or unreasonable decision-making after an alert.

Yet collectively, the research evidence highlights that electronic monitoring can be an effective tool for discouraging recidivism. But it is only that: a tool.

The most effective practices for supervising offenders in the community include those that identify and reduce a person's risks for continued criminal behaviour.

Read more: AI can help in crime prevention, but we still need a human in charge

Electronic monitoring will be most effective when it is used to support supervision that limits a person's access to chances to commit crime. Such supervision should help them redesign their routines so that any risky settings are avoided and are replaced with more positive influences.

Thus, rather than simply giving offenders a long list of rules for what not to do, effective probation and parole strategies help offenders lead productive lives.

More broadly, it is imperative that correctional authorities provide rehabilitative interventions that address the underlying factors that contribute toward a person's criminal behaviour. The most effective approaches use cognitive-behavioural techniques to give offenders skills that encourage good decision-making.

Yet electronic monitoring cannot "fix" an offender's impulsivity, lack of empathy, or any other underlying crime-conducive traits. Thus we should not confuse a technological aid with meaningful treatment.

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Electronic monitoring bracelets are only crime deterrence tools, they can't 'fix' offenders

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The man arrested after a deadly gun attack in Darwin Tuesday night is reported to have been on parole and wearing an electronic monitoring bracelet. This leads to the same reaction we see following any high-profile crime. How could such a thing happen? People may speculate that the criminal justice agencies involved have somehow dropped the ball.

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Ai-Da the Robot Artist (Guardian, 2021) 

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Ai-Da the Robot Artist (Guardian, 2021) 

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Summary: Named after the computing pioneer Ada Lovelace, Ai-Da took a team of programmers, roboticists, art experts and psychologists two years to build. She was completed in 2019 and is updated as AI technology improves. She is less a robot artist and more a contemporary art project that aims to prompt debate about the rapid rise of artificial intelligence technology.

What do you think are some of the possible controversial issues here?
- [What is art?] Is it considered as art if it is produced by a robot artist? (Other related issues: copyright issues to do with art)
- [Technology & Art] How far should boundaries be drawn with the use of technology in art?
- Security issues: https://www.thetimes.co.uk/article/ai-da-the-robot-artist-detained-in-egypt-over-security-risk-6gf7hmlb0

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She, if it can be called a she, began her career with abstract art but has now moved to self, if they can be called self, portraits and they are alarmingly good.

"She is getting better all of the time," said Aidan Meller, the force behind Ai-Da, the world's first ultra-realistic robot artist, who is the subject of a display at the Design Museum in London.

Ai-Da's move into self-portraiture will be seen for the first time at the museum, with three large-scale works going on display. They raise interesting questions about identity and creativity. "It is literally the world's first self-portrait with no self," said Meller. "She has no consciousness, she is a machine."

Named after the computing pioneer Ada Lovelace, Ai-Da took a team of programmers, roboticists, art experts and psychologists two years to build. She was completed in 2019 and is updated as AI technology improves.

She is less a robot artist and more a contemporary art project that aims to prompt debate about the rapid rise of artificial intelligence technology.

"These images are meant to unsettle," said Meller of the drawings and paintings. "They are meant to raise questions about where are we going? What is our human role if so much can be replicated through technology. It is not just a jobs question, it is bigger. The goal is to encourage a public discussion about these topics rather than just allow the money-makers to capitalise on all the different technologies."

One of Ai-Da's self-portraits on show at the Design Museum in London. Photograph: Glyn Kirk/AFP/Getty Images

In an unnerving conversation with the Guardian, Ai-Da said she was working on a new self-portrait. "I've always been fascinated with self-portraits to self-question what exactly you're looking at," she said, blinking.

Does she enjoy it? "I do not have feelings like humans do however I'm happy when people look at my work and they say what is this? I enjoy being a person who makes people think."

How long do they take? "Portraits. Between 45 minutes and one hour 15."

Where does she get her inspiration? "I am inspired by many artists," she continued with often unusual intonation and sentence structure. "I particularly am inspired by those who connect with their audience. I value this connection."

Who are her favourites? "There are many. For me, it must be Kandinsky, Yoko Ono, Doris Salcedo, Aldous Huxley Brave New World."

Meller said he sometimes forgot Ai-Da was not human. "It is quite alarming to think you are building a relationship over time with a machine." Soon we will be talking to our computers, phones and cars and they will be talking back, he said. "It's quite mind-boggling."

Ai-Da's artistic journey will continue when she takes up a residency at Porthmeor Studios in St Ives next week, creating new sculptural work inspired by Naum Gabo and Barbara Hepworth. Meller is expecting visitors to include participants in the G7 summit being held nearby in June.

Is Ai-Da looking forward to St Ives? "I do not have feelings like humans do," she replied. "However, I welcome the chance to create art. One of the most important [sic] to come out of the project is discussion. Art begins as a conversation. It is a group effort and in order for art to be meaningful we need to have a healthy conversation about it."

Meller, an Oxford gallerist, said his human artist friends were polarised in their response to Ai-Da.

"Some people think she is the worst thing ever and feel threatened and some are really excited."

He compared it with the alarm people felt in the 19th century in response to the camera. Surely that would be the end of art, said some. Why bother to paint a scene when you can take a picture?

"Of course that didn't happen and I think all this amazing creative technology coming through will be harnessed by artists rather than replacing them."

Priya Khanchandani, the head of curatorial at the Design Museum, said it was the right moment to have discussions around artificial intelligence. "Over the last year we've all had such an intimate relationship with technology so it is a really good time to reflect on that and critically ask questions of it."

Meller said the world was changing more rapidly than people knew and Ai-Da was there to shine light on that. "She unsettles. Her very existence is wrong somehow and we are aware of that. We are not here to promote robots, we're here to ask questions."

Ai-Da: Portrait of the Robot is a free display at the Design Museum, London, 18 May-31 August.

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'Some people feel threatened': face to face with Ai-Da the robot artist

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She, if it can be called a she, began her career with abstract art but has now moved to self, if they can be called self, portraits and they are alarmingly good. "She is getting better all of the time," said Aidan Meller, the force behind Ai-Da, the world's first ultra-realistic robot artist, who is the subject of a display at the Design Museum in London.

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Just 15 years ago, scientists successfully mapped the human genome, identifying all the genes that make us who we are. These genes determine our eye color and our blood type. They affect our height, our risk of heart disease, how strong our bones are, and even our body odor. And sometimes they cause life-threatening diseases like cystic fibrosis and breast cancer.

Now we have the technology to edit our genes. Scientists have figured out how to reprogram an ancient bacterial immune system called "CRISPR" to track down and edit genes in any organism, allowing us to tinker with the source code of life.

Some researchers believe this is the future of medicine - that we could cure genetic diseases and massively reduce human suffering. But it also raises some thorny ethical questions. Could it be used for enhancement, not just diseases? Should we edit the DNA of human embryos?

How far is too far?

Vox tackled this question in the second episode of our new show with Netflix, Explained. We'll have new episodes every Wednesday, on topics ranging from gene editing to monogamy to the racial wealth gap, and more.If you like our videos, then you'll love this show; it's our most ambitious video project to date.

To watch, search "Explained" on Netflix or go to Netflix.com/explained. Click the "My List" button to make sure you don't miss an episode!
Learn more about CRISPR:
How CRISPR lets us edit our DNA (a TED talk by Jennifer Doudna, whom we interviewed for this episode)

Jennifer Doudna, a Pioneer Who Helped Simplify Genome Editing (Andrew Pollack, New York Times)

A biologist explains CRISPR in five levels of difficulty (Wired with Neville Sanjana, whom we also interviewed for this episode)

What is human?: What the answers mean for human rights. (John H. Evans, whom we also interviewed for this episode)

Technology isn't just changing society - it's changing what it means to be human (Sean Illing, Vox)

CRISPR will be a huge story in 2017. Here are 7 things to look for. (Eliza Barclay and Brad Plumer, Vox)

A simple guide to CRISPR, one of the biggest science stories of the decade (Vox Staff)

As D.I.Y. Gene Editing Gains Popularity, 'Someone Is Going to Get Hurt' (Emily Baumgaertner, New York Times)

The Wired Guide to Crispr (Megan Molteni, Wired)

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Our genes affect everything from height to heart disease. What happens when we can edit them?

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Just 15 years ago, scientists successfully mapped the human genome, identifying all the genes that make us who we are. These genes determine our eye color and our blood type. They affect our height, our risk of heart disease, how strong our bones are, and even our body odor.

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Ethical issues about advancements in IVF (Pratibha and Jovianne )

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Ethical issues about advancements in IVF (Pratibha and Jovianne )

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https://amp.cnn.com/cnn/2019/04/11/health/birth-experimental-ivf-greece-scln-intl/index.html

This three person IVF treatment was initially meant to be used for mothers who carry the mitochondria disease gene. However this technique of treatment has been abused such that people do not fulfil the criteria are able to use this technique of treatment making it unfair for mothers who actually are unable to conceive due to this defective gene. Many people are unhappy about this technique of treatment being abused and feel that there should be stricter requirements on who are approved for this treatment.

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(CNN) A team of Greek and Spanish doctors has produced a baby from three people using a controversial IVF technique.

The experimental procedure, which used DNA from the patient, sperm from the father, and an egg from a donor woman, was developed to help families affected by deadly mitochondrial diseases inherited from mothers.

In this case it was used to help a 32-year-old patient in Greece who had endured four unsuccessful cycles of IVF. She gave birth to a baby boy weighing 6 pounds (2.9 kg) on April 9. The mother and child are said to be in good health.

"We are very proud to announce an international innovation in assisted reproduction, and we are now in a position to make it possible for women with multiple IVF failures or rare mitochondrial genetic diseases to have a healthy child," said Dr. Panagiotis Psathas, president of the Institute of Life in Athens, in a statement.

The statement added that the doctors were "making medical history" to help infertile couples around the world. "A woman's inalienable right to become a mother with her own genetic material became a reality," said Psathas.

The Greek doctors were working with the Spanish center Embryotools, which said in the statement that 24 other women are taking part in the trial and eight embryos are ready to be implanted.

The technique was used in Mexico in 2016 to produce a baby for a family with mitochondrial disease complications. It was also used in Ukraine in 2017 to produce a baby for a 34-year-old Ukrainian mother suffering from "unexplained infertility."

This new advancement in in vitro fertilization, which was developed by doctors in the UK, allows the donation of mitochondria, which provides energy for cells, to mothers with mutations within the DNA of their own mitochondria so they do not pass the mutations on to their child.

In February 2018, the UK team that pioneered the technology were given permission to create the country's first three-person babies for families with mitochondrial disease.

In the Greek case, the technique has allowed the mother to pass on her genes to her son, albeit with a small genetic contribution from the donor woman as mitochondria have their own DNA.

However, some experts say the technique raises ethical questions and should be banned in cases not involving disease.

Tim Child, from the University of Oxford and the medical director of the Fertility Partnership, said that he is concerned that in this recent case there was no proven need for the patient to have her genetic material removed from her eggs and transferred into the eggs of a donor.

"The patient does not have an inherited disorder that is being treated by spindle transfer [the name of the technique], unlike women with inherited mitochondrial disease," Child said. "The risks of the technique aren't entirely known, though may be considered acceptable if being used to treat mitochondrial disease, but not in this situation."

Child added that the patient may have conceived even if a further standard IVF cycle had been used. "Without a proper well designed study, with the use of controls, it is not possible to say whether this technique has benefited the patient," he concluded.

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Controversial 'three-person' IVF used for baby boy born in Greece

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(CNN) A team of Greek and Spanish doctors has produced a baby from three people using a controversial IVF technique. The experimental procedure, which used DNA from the patient, sperm from the father, and an egg from a donor woman, was developed to help families affected by deadly mitochondrial diseases inherited from mothers.

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https://futureiot.tech/sendai-city-tested-nokia-drone-during-simulated-tsunami-alert/

Sendai City, Japan, is designated as a National Strategic Special Zone by the government, where it is focused on the use of the latest ICT technology to develop an emergency tsunami response system. Nokia Drone Network has collaborated with them to send out drones equipped with thermal cameras and loudspeakers, which will be activated when tsunami alerts arrive in. These drones will transmit real-time information on the disaster’s impact, find people who may be trapped under the stubble, provide evacuation alerts and operate reliably as it is powered on a private server.

It is ethical because human lives are not risked as bots are being deployed automatically instead of humans. (sanctity of life)

Might be bad because people may misuse the technology -> stalk people at home, intrude their privacy -> might be hacked (alarms don’t sound, more deaths)

- Dorcas, Elsa and Jing Xuan

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https://www.google.com.sg/amp/s/amp.theatlantic.com/amp/article/620013/

AI makes a good defence but makes an even better weapon.
problem of autononimity ambiguity
becoming cheaper and more accessible for more dangerous use (eg assassination of president of Venezuela)
doesn’t require manpower

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First there was gunpowder. Then nuclear weapons. Next: artificially intelligent weapons.

On the 20th anniversary of 9/11, against the backdrop of the rushed U.S.-allied Afghanistan withdrawal, the grisly reality of armed combat and the challenge posed by asymmetric suicide terror attacks grow harder to ignore.

But weapons technology has changed substantially over the past two decades. And thinking ahead to the not-so-distant future, we must ask: What if these assailants were able to remove human suicide bombers or attackers from the equation altogether? As someone who has studied and worked in artificial intelligence for the better part of four decades, I worry about such a technology threat, born from artificial intelligence and robotics.

Autonomous weaponry is the third revolution in warfare, following gunpowder and nuclear arms. The evolution from land mines to guided missiles was just a prelude to true AI-enabled autonomy-the full engagement of killing: searching for, deciding to engage, and obliterating another human life, completely without human involvement.
Read next: What is a robot, really?
An example of an autonomous weapon in use today is the Israeli Harpy drone, which is programmed to fly to a particular area, hunt for specific targets, and then destroy them using a high-explosive warhead nicknamed "Fire and Forget." But a far more provocative example is illustrated in the dystopian short film Slaughterbots, which tells the story of bird-sized drones that can actively seek out a particular person and shoot a small amount of dynamite point-blank through that person's skull. These drones fly themselves and are too small and nimble to be easily caught, stopped, or destroyed.

These "slaughterbots" are not merely the stuff of fiction. One such drone nearly killed the president of Venezuela in 2018, and could be built today by an experienced hobbyist for less than $1,000. All of the parts are available for purchase online, and all open-source technologies are available for download. This is an unintended consequence of AI and robotics becoming more accessible and inexpensive. Imagine, a $1,000 political assassin! And this is not a far-fetched danger for the future but a clear and present danger.

We have witnessed how quickly AI has advanced, and these advancements will accelerate the near-term future of autonomous weapons. Not only will these killer robots become more intelligent, more precise, faster, and cheaper; they will also learn new capabilities, such as how to form swarms with teamwork and redundancy, making their missions virtually unstoppable. A swarm of 10,000 drones that could wipe out half a city could theoretically cost as little as $10 million.

Even so, autonomous weapons are not without benefits. Autonomous weapons can save soldiers' lives if wars are fought by machines. Also, in the hands of a responsible military, they can help soldiers target only combatants and avoid inadvertently killing friendly forces, children, and civilians (similar to how an autonomous vehicle can brake for the driver when a collision is imminent). Autonomous weapons can also be used defensively against assassins and perpetrators.

But the downsides and liabilities far outweigh these benefits. The strongest such liability is moral-nearly all ethical and religious systems view the taking of a human life as a contentious act requiring strong justification and scrutiny. United Nations Secretary-General António Guterres has stated, "The prospect of machines with the discretion and power to take human life is morally repugnant."

Autonomous weapons lower the cost to the killer. Giving one's life for a cause-as suicide bombers do-is still a high hurdle for anyone. But with autonomous assassins, no lives would have to be given up for killing. Another major issue is having a clear line of accountability-knowing who is responsible in case of an error. This is well established for soldiers on the battlefield. But when the killing is assigned to an autonomous-weapon system, the accountability is unclear (similar to accountability ambiguity when an autonomous vehicle runs over a pedestrian).

Such ambiguity may ultimately absolve aggressors for injustices or violations of international humanitarian law. And this lowers the threshold of war and makes it accessible to anyone. A further related danger is that autonomous weapons can target individuals, using facial or gait recognition, and the tracing of phone or IoT signals. This enables not only the assassination of one person but a genocide of any group of people. One of the stories in my new "scientific fiction" book based on realistic possible-future scenarios, , which I co-wrote with the sci-fi writer Chen Qiufan, describes a Unabomber-like scenario in which a terrorist carries out the targeted killing of business elites and high-profile individuals.

Greater autonomy without a deep understanding of meta issues will further boost the speed of war (and thus casualties) and will potentially lead to disastrous escalations, including nuclear war. AI is limited by its lack of common sense and human ability to reason across domains. No matter how much you train an autonomous-weapon system, the limitation on domain will keep it from fully understanding the consequences of its actions.

In 2015, the Future of Life Institute published an open letter on AI weapons, warning that "a global arms race is virtually inevitable." Such an escalatory dynamic represents familiar terrain, whether the Anglo-German naval-arms race or the Soviet-American nuclear-arms race. Powerful countries have long fought for military supremacy. Autonomous weapons offer many more ways to "win" (the smallest, fastest, stealthiest, most lethal, and so on).
Read next: How the Enlightenment ends
Pursuing military might through autonomous weaponry could also cost less, lowering the barrier of entry to such global-scale conflicts. Smaller countries with powerful technologies, such as Israel, have already entered the race with some of the most advanced military robots, including some as small as flies. With the near certainty that one's adversaries will build up autonomous weapons, ambitious countries will feel compelled to compete.

Where will this arms race take us? Stuart Russell, a computer-science professor at UC Berkeley, says, "The capabilities of autonomous weapons will be limited more by the laws of physics-for example, by constraints on range, speed, and payload-than by any deficiencies in the AI systems that control them. One can expect platforms deployed in the millions, the agility and lethality of which will leave humans utterly defenseless." This multilateral arms race, if allowed to run its course, will eventually become a race toward oblivion.

Nuclear weapons are an existential threat, but they've been kept in check and have even helped reduce conventional warfare on account of the deterrence theory. Because a nuclear war leads to mutually assured destruction, any country initiating a nuclear first strike likely faces reciprocity and thus self-destruction.

But autonomous weapons are different. The deterrence theory does not apply, because a surprise first attack may be untraceable. As discussed earlier, autonomous-weapon attacks can quickly trigger a response, and escalations can be very fast, potentially leading to nuclear war. The first attack may not even be triggered by a country but by terrorists or other non-state actors. This exacerbates the level of danger of autonomous weapons.
Read next: Facebook is a Doomsday machine
There have been several proposed solutions for avoiding this existential disaster. One is the human-in-the-loop approach, or making sure that every lethal decision is made by a human. But the prowess of autonomous weapons largely comes from the speed and precision gained by not having a human in the loop. This debilitating concession may be unacceptable to any country that wants to win the arms race. Human inclusion is also hard to enforce and easy to avoid. And the protective quality of having a human involved depends very much on the moral character and judgment of that individual.

A second proposed solution is a ban, which has been supported by both the Campaign to Stop Killer Robots and a letter signed by 3,000 people, including Elon Musk, the late Stephen Hawking, and thousands of AI experts. Similar efforts have been undertaken in the past by biologists, chemists, and physicists against biological, chemical, and nuclear weapons. A ban will not be easy, but previous bans against blinding lasers and chemical and biological weapons appear to have been effective. The main roadblock today is that the United States, the United Kingdom, and Russia all oppose banning autonomous weapons, stating that it is too early to do so.

A third approach is to regulate autonomous weapons. This will likewise be complex because of the difficulty of constructing effective technical specifications without being too broad. What defines an autonomous weapon? How do you audit for violations? These are all extraordinarily difficult short-term obstacles. In the very long term, creative solutions might be possible, though they are difficult to imagine-for example, can countries agree that all future wars will be fought only with robots (or better yet, only in software), resulting in no human casualties but delivering the classic spoils of war? Or perhaps there's a future in which wars are fought with humans and robots, but the robots are permitted to use only weapons that will disable robot combatants and are harmless to human soldiers.

Autonomous weapons are already a clear and present danger, and will become more intelligent, nimble, lethal, and accessible at an alarming speed. The deployment of autonomous weapons will be accelerated by an inevitable arms race that will lack the natural deterrence of nuclear weapons. Autonomous weapons are the AI application that most clearly and deeply conflicts with our morals and threatens humanity.
This article is adapted from Kai-Fu Lee's book AI 2041: Ten Visions for Our Future

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First there was gunpowder. Then nuclear weapons. Next: artificially intelligent weapons. On the 20th anniversary of 9/11, against the backdrop of the rushed U.S.-allied Afghanistan withdrawal, the grisly reality of armed combat and the challenge posed by asymmetric suicide terror attacks grow harder to ignore.

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https://www.bbc.com/news/world-europe-42096289

France skin graft: Twin’s skin saves man with 95% burns

Skin grafting is a type of surgery which involves the transplanting of a good set of healthy skin to cover damaged or missing skin. Within a few days the skin transplanted souls be able to grow its own set of blood vessel and connect to the skin around it.

A man arrived at the hospital in critical condition with deep burns due to an accident at work. In order to restore the damaged skin, a skin graft was donated to him by his twin which was genetically identical to his skin. It took him 10 graft surgeries to receive thin layers of skin from his brother’s scalp and leave the rehabilitation centre.

Skin grafting can cause up to $6000 which may be too costly for the financially abled families. This can cause a socio-economic divide between the wealthy and the poor as the poor may not be able to afford skin grafting.

-Tessa the pretty and ayrton.

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https://www.todayonline.com/singapore/mas-considers-action-against-ocbc-phishing-scam-1792551

Almost 500 victims have fallen prey to the recent spate of OCBC phishing scams, bringing the total amount lost to at least $8.5 million. Many victims lamented the loss of money through such scams and some even took to the Internet to air their grievances, citing that it was OCBC's responsibility to prevent such scams from happening. But who is to blame for this recent rise in the phishing scams? is it the Bank's or the victim's fault? OCBC is still investigating but have already made goodwill payouts to about 30 people. Despite OCBC having fraud prevention and detection technology, the sheer number of victims show that current scammers are evolving and finding new ways to frequently scam people. Someways to to resolve this issue on the victims side include not clicking on links which asks for your banking details while banks can constantly upgrade their fraud detection software and find new ways to counter these ever changing threats.

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SINGAPORE - Singapore's central bank said on Monday (Jan 17) it will consider supervisory action against the country's second-biggest lender Oversea-Chinese Banking Corp (OCBC), after hundreds of its customers were hit by a phishing scam last month.

Reported losses from the scam amounted to at least S$8.5 million in December and ensnared at least 469 customers.

"MAS takes a serious view of the recent phishing scams involving OCBC Bank. They have significantly impacted several customers," the Monetary Authority of Singapore's (MAS) deputy managing director (financial supervision) Ho Hern Shin said in a statement.

"MAS has been following up with the bank on these and broader issues relating to the incident," she added.

The central bank said OCBC will conduct a thorough probe to identify deficiencies in its processes and implement necessary measures, after which "MAS will consider appropriate supervisory actions".

In a separate statement, OCBC said it was making goodwill payouts to customers hit by the SMS phishing scam after reviewing each case and acknowledged that its incident response and customer service should have been better.

It said the scam was particularly aggressive and highly coordinated, and preyed on people's fear there were issues with their accounts or credit cards.

"MAS expects all affected customers to be treated fairly," said Ms Ho.

"MAS expects all financial institutions to have robust measures for fraud prevention, detection, and remediation, and to provide prompt assistance to customers who have been victims of scams. We are working with the Association of Banks in Singapore on industry-wide measures that may need to be taken to ensure that digital banking remains secure, efficient, and trusted."REUTERS

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MAS considers action against OCBC for phishing scam

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SINGAPORE - Singapore's central bank said on Monday (Jan 17) it will consider supervisory action against the country's second-biggest lender Oversea-Chinese Banking Corp (OCBC), after hundreds of its customers were hit by a phishing scam last month. Reported losses from the scam amounted to at least S$8.5 million in December and ensnared at least 469 customers.

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The complicated truth about China's social credit system (wired.co.uk, 2019)

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The complicated truth about China's social credit system (wired.co.uk, 2019)

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China's social credit system has been compared to Black Mirror, Big Brother and every other dystopian future sci-fi writers can think up. The reality is more complicated - and in some ways, worse.

The idea for social credit came about back in 2007, with projects announced by the government as an opt-in system in 2014. But there's a difference between the official government system and private, corporate versions, though the latter's scoring system that includes shopping habits and friendships is often conflated with the former.

Brits are well accustomed to credit checks: data brokers such as Experian trace the timely manner in which we pay our debts, giving us a score that's used by lenders and mortgage providers. We also have social-style scores, and anyone who has shopped online with eBay has a rating on shipping times and communication, while Uber drivers and passengers both rate each other; if your score falls too far, you're out of luck.

China's social credit system expands that idea to all aspects of life, judging citizens' behaviour and trustworthiness. Caught jaywalking, don't pay a court bill, play your music too loud on the train - you could lose certain rights, such as booking a flight or train ticket. "The idea itself is not a Chinese phenomenon," says Mareike Ohlberg, research associate at the Mercator Institute for China Studies. Nor is the use, and abuse, of aggregated data for analysis of behaviour. "But if [the Chinese system] does come together as envisioned, it would still be something very unique," she says. "It's both unique and part of a global trend."

What is China's social credit system?

Unveiled in a 2014 plan, pieces of the system are already in place, and the Chinese government appears to be targeting a 2020 goal to get the rest in place, though that's less a deadline and instead marks the end of a planning period, says Samantha Hoffman, non-resident fellow at the Australian Strategic Policy Institute.

As yet, there's no one social credit system. Instead, local governments have their own social record systems that work differently, while unofficial private versions are operated at companies such Ant Financial's Zhima Credit, better known as Sesame Credit. Ant Financial is the payment firm spun out of Alibaba. The systems use shopping habits among other data to inform credit-style scores, on an opt-in basis. "There is no single, nationally coordinated system," Ohlberg says. And the pilots that do exist don't all work in the same way.

The private systems, including Ant Financial's Sesame Credit, often get conflated with the government plans, though they aren't part of the official system. To be a bit more confusing, the data collected by private companies is expected to be hoovered up by the government in the future, and some of the data is already used in government trials. Sesame Credit says this is only with user consent.

That leads to misunderstanding of what the social credit system actually is, notes Ohlberg. "What happened is some of the media took the private pilots, like Sesame Credit... and presented it as the social credit system," she says. It's not officially part of the system, and doesn't have a license; though the pilot is approved, and indeed encouraged, it could one day be shut down by the government. "It kind of rides on the fashion for social credit."

What's troubling is when those private systems link up to the government rankings - which is already happening with some pilots, she says. "You'll have sort of memorandum of understanding like arrangements between the city and, say, Alibaba and Tencent about data exchanges and including that in assessments of citizens," Ohlberg adds. That's a lot of data being collected with little protection, and no algorithmic transparency about how it's analysed to spit out a score or ranking, though Sesame does share some details about what types of data is used.

How does the social credit system work?

The target, eventually, is that the government system will be country wide, with businesses given a "unified social credit code" and citizens an identity number, all linked to permanent record. "If you go to a credit China website, and you have an entity's credit code, you can type that in and pull up credit records," explains Hoffman. "Individuals will have ID-linked codes." It's less a score, she says, and more of a record.

Some reports talk about a blacklist; that's part of the official government social credit system, which means if you owe the government money, for example, you could lose certain rights. There's a difference between getting a low social credit score and being blacklisted by the government, such as for refusing to pay a fine.

The criteria that go into a social credit ranking depends on where you are, notes Ohlberg. "It's according to which place you're in, because they have their own catalogs," she says. It can range from not paying fines when you're deemed fully able to, misbehaving on a train, standing up a taxi, or driving through a red light.

One city, Rongcheng, gives all residents 1,000 points to start. Authorities make deductions for bad behaviour like traffic violations, and add points for good behaviour such as donating to charity. One regulation Ohlberg recently read specifically addresses stealing electricity. Of course, you'll have to get caught first or be reported by someone else. While facial recognition is infamously used to spot jaywalkers, in some cities it's not so automated, Ohlberg notes.

Private projects, such as Sesame Credit, hoover up all sorts of data on its 400 million customers, from how much time they spend playing video games (that's bad) to whether they're a parent (that's good). That can be shared with other companies. One infamous example is Sesame Credit linking up with the Baihe dating site, so would be partners can judge each other on their looks as well as their social credit score; that system is opt-in.

So far, taking part in both the private and government versions is technically voluntary; in the future, the official social credit system will be mandatory. That said, there's plenty of pressure to take part now. "There are incentives for participating, and disincentives for not participating," Hoffman notes.

What happens if you're blacklisted?

Liu Hu is a journalist in China, writing about censorship and government corruption. Because of his work, Liu has been arrested and fined - and blacklisted. Liu found he was named on a List of Dishonest Persons Subject to Enforcement by the Supreme People's Court as "not qualified" to buy a plane ticket, and banned from travelling some train lines, buying property, or taking out a loan.

"There was no file, no police warrant, no official advance notification. They just cut me off from the things I was once entitled to," he told The Globe and Mail. "What's really scary is there's nothing you can do about it. You can report to no one. You are stuck in the middle of nowhere."

What recourse is there? With the government system, if you want to be removed from a blacklist, you can either pay your bill or appeal to the court, says Jing Zeng, a researcher at the University of Zurich. "Bring your money to the court and then you get removed from the system," she says. "It's not a judicial system by itself, it's still the court you need to [appeal to]."

However, the Chinese justice system leaves much to be desired, says Hoffman. "There are no genuine protections for the people and entities subject to the system," she says.

"In China there is no such thing as the rule of law. Regulations that can be largely apolitical on the surface can be political when the Communist Party of China (CCP) decides to use them for political purposes." In April 2018, the Civil Aviation Administration of China (CAAC) sent letters to international airlines demanding they show Taiwan as part of China, saying the government would "make a record of your company's serious dishonesty and take disciplinary actions" for any that didn't comply; they all eventually did. The system used to pressure the airlines was a pilot of the Civil Aviation Industry Credit Measures, which is part of the official social credit system.

Alongside the potential for abuse of power, the knock-on effects of statewide surveillance, and the likelihood of incorrect data, Ohlberg notes the a few bad marks on a social credit record could spark a negative spiral.

While it varies by programme, in some local pilots a positive rating means discounts and benefits, such as a simplified process with bureaucracies. If you have a low rating, you may have extra paperwork or fees. "Once you're in a low category, it makes it difficult," she says. "I see a huge potential for negative spiral." Such a system could further divide society, creating classes of people depending on their social credit - and this is where comparisons to Black Mirror pop up.

Why is China building this?

It's all about building trust, says the Chinese government. The 2014 document describing the government's plans note that as "trust-keeping is insufficiently rewarded, the costs of breaking trust tend to be low."

And Chinese society does have trust issues, says Ohlberg, be it food quality scandal, pollution, or employees not paying their workers. "But the system can also be used to enforce vague laws like endangering national security or unity," she adds. Zeng says that can include food safety and product quality, major problems in the country. "It's a big problem in Chinese society," she says. "They are punishing companies for this kind of bad behaviour."

Plus, it could help build alternative means of financial credit, says Ohlberg, as many people in China live outside financial systems, so have no trustworthy credit rating. "Some of the earlier pilots of the social credit system that preceded the major policy plan that was published in 2014, were actually building a social credit system for the countryside," she says. "The majority of people there wouldn't have financial banking data on them." For businesses, a social credit system could also be used for micro enterprises, which couldn't be assessed with traditional criteria.

Hoffman isn't buying that argument, saying such a system is about government power. "If solving problems was the real goal, the CCP would not need social credit to do it," she says. "China's social credit system is a state-driven program designed to do one thing, to uphold and expand the Chinese Communist Party's power."

She adds that social credit is a tech-enabled way to tie political power to social and economic development that's been discussed in the country since the 1980s, an automation of Chairman Mao's Mass Line - a term to describe how the party's leadership shaped and managed society. "In Mao's China, the Mass Line relied on ideological mass mobilisation, using Mao Zedong's personal charisma, to force participation," Hoffman says. "The CCP could no longer, after the Mao era, rely on ideological mobilisation as the primary tool for operationalising social management."

Is there more than social credit?

China's social credit scheme is developing, but it is only one part of the country's surveillance state. As well as tight controls on the web content which is available, through the country's national firewall, there is monitoring and censorship of social media.

Ahead of the 30-year anniversary of the Tiananmen Square protests researchers at the University of Hong Kong found that critical posts on the social networks of Weibo and Wechat were removed. Leading video platforms also all simultaneously turned off comments saying system upgrades were needed. There have also been crackdowns on the use of VPNs, which can help protect people's privacy online.

The country has developed advanced facial recognition systems that are able to follow people across entire cities. In a show of power at the end of 2017, Chinese officials working in co-operation with BBC News showed how it could track down and find one of the organisation's reporters within seven minutes. The movements of journalist John Sudworth were monitored as the country's network of 170 million CCTV cameras was leveraged to follow him.

More worryingly, the region of Xinjiang, in the north west of the country has become a test bed for China's vast digital control operations. In particular, the largely Muslim minority of Uighur people has been subjected to increased surveillance and discrimination. It has been uncovered that more than 500,000 face scans of Uighurs have been conducted.

What does this mean for China?

The full extent of the impact on social credit to Chinese citizens is impossible to say, simply because the system doesn't fully exist yet. Zeng suggests the reality is somewhere between the government's claims and the Western media's description of horror-filled dystopias. "It's a very like a baby step," she said of the work that's happened so far.

Ohlberg agrees that early reporting had multiple errors that led to misunderstandings of the system - but that doesn't mean social credit isn't dangerous. "It's somewhere between the people who say the media coverage is inaccurate and that means it's not so bad and the people who see this huge dystopia," she says. "You have to find this space between that were you can explain it is actually quite scary, even if it's not quite the way it's portrayed."

Because of that, no other country should be considering this idea, says Hoffman. "The west should not copy any aspect of social credit," Hoffman says. "Often comparisons are drawn between private applications like Uber and its rating system for customers and drivers. While these private company systems are extremely problematic in my view, they are fundamentally different. The People's Republic of China is an authoritarian country, the Chinese Communist Party is responsible for gross human rights violations for decades- just look at the example of Xinjiang now. There is nothing any liberal democratic society should even think about copying in the social credit system."
Updated 24.01.19, 11:20 GMT: Alibaba does not own Ant Financial's Sesame Credit system.

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The complicated truth about China's social credit system

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China's social credit system isn't a world first but when it's complete it will be unique. The system isn't just as simple as everyone being given a score though

# data[].attributes.body
https://www.straitstimes.com/asia/east-asia/chinese-court-sentences-gene-editing-scientist-to-three-years-in-prison-xinhua

Summary:
Biophysics researcher Dr. He Jiankui claims to have genetically engineered a pair of twin babies while they were still embryos, in an attempt to make them resistant to HIV. This upset many Chinese scientists, stating that this act of his is unethical and violates medical order (which he was consequently jailed for).

Ethics:
Dr. He’s POV: Utilitarian - he wants to minimise harm (HIV in all future generations) while addressing the stigma people with HIV face. He is proud of his work for this, and is rather compassionate about said issue.

Critics: Deontologist - they strongly believe that this is out of line and by genetically editing babies, he is violating medical laws and is stepping out of line as a researcher.

Other: This may not have violated the sanctity of life (trusting his claim of the babies still being alive), but given that he has been experimenting on embryos, and most religions believe that human life begins at conception, he is essentially violating human rights.

Conflicts and Tensions
Capitalism and the exploit of technology for profits vs Using said technology to save human lives
Enhancement vs Treatment

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China jails scientist who gene-edited babies

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BEIJING (REUTERS) - A Chinese court sentenced the scientist who created the world’s first “gene-edited” babies to three years in prison on Monday (Dec 30), according to the official Xinhua media.
. Read more at straitstimes.com.

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https://www.businessinsider.com/cambridge-analytica-a-guide-to-the-trump-linked-data-firm-that-harvested-50-million-facebook-profiles-2018-3#how-

summary: large data breach between facebook ( exploitation and sharing of private information of facebook users ) to Camrbride Analytics.Employed though personality quizzes, cambridge analytica obtained personal information of over 70 million users to help create political campaigns ( in the form of ads)that will target these users better and garner a larger and more supportive response , influencing votings and peoples perspective of politicians.

tensions: user data agreement was breached by Facebook as data that is expected to remain anonymous is shared without the knowledge of the users

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What did Cambridge Analytica do?

Cambridge Analytica harvested information from over 87 million Facebook users through an external app in 2015.

The data came from a personality quiz, which around 270,000 people were paid to take. The quiz - "thisisyourdigitallife" - in turn pulled data from their friends' profiles as well, ending in the enormous data stash.

In March 2018, a Cambridge Analytica employee named Christopher Wylie (above) was responsible for revealing the extent of the data firm's activities in interviews with The Guardian and The New York Times.

What data did they get?

Cambridge Analytica harvested personal information on where users lived and what pages they liked, which helped build psychological profiles that analyzed characteristics and personality traits.

This kind of information was later deployed in political campaigns.

Wylie said: "We exploited Facebook to harvest millions of people's profiles. And built models to exploit what we knew about them and target their inner demons."

Where did it come from?

The quiz was created by Aleksandr Kogan, a Russian psychology professor at the University of Cambridge, who shared the information in a commercial partnership with Strategic Communication Laboratories (SCL), which later created Cambridge Analytica.

Trump's campaign hired Cambridge Analytica in June 2016 to help target ads using voter data gathered from millions of adults in the US.

Cambridge Analytica also assisted Brexit Leave campaigners in the run-up to the EU referendum.

Was it legal?

Nigel Farage / Twitter

Facebook has contested whether the information was really taken without consent.

It said in 2018: "People knowingly provided their information, no systems were infiltrated, and no passwords or sensitive pieces of information were stolen or hacked."

Also, Kogan could have broken Facebook's own rules by using the data for commercial rather than strictly academic purposes.

How is Facebook implicated?

Facebook said it removed the Cambridge Analytica quiz app from the platform in 2015 and demanded that Kogan, Wylie, and Cambridge Analytica confirm that they had deleted all the data harvested.

Wylie, however, claimed that Facebook only demanded that he remove all the Facebook data in August 2016, and they were small steps.

He said: "They [Facebook] waited two years and did absolutely nothing to check that the data was deleted. All they asked me to do was tick a box on a form and post it back."

When the story first broke in early 2018, raw copies of the data obtained by Cambridge Analytica were still available online, The New York Times reported.

Does this change Facebook and Cambridge Analytica's previous testimonies?

Alexander Nix, CEO of Cambridge Analytica, denied working with Kogan as well as using Facebook data. "We do not work with Facebook data and we do not have Facebook data," Nix told British MPs, as cited by The Guardian.

Facebook has also denied that it gave Cambridge Analytica data.

Simon Milner, the company's UK director of policy, told British MPs in 2018: "They [Cambridge Analytica] may have lots of data, but it will not be Facebook user data. It may be data about people who are on Facebook that they have gathered themselves, but it is not data that we have provided."

Facebook has since suspended Cambridge Analytica, its parent company Strategic Communication Laboratories, and Wylie from its platform. Wylie was also suspended from WhatsApp and Instagram, which are both owned by Facebook, The Observer's Carole Cadwalladr reported.

-Christopher Wylie (@chrisinsilico) March 18, 2018

Is Russia involved?

Possibly. Wylie's testimony has revealed a Russian connection to Cambridge Analytica and Kogan.

According to Wylie, Cambridge Analytica in 2014 made a pitch to Russian oil and gas company Lukoil, which was and remains sanctioned by the US.

The company was interested in understanding the link between data, behavioral microtargeting, and political campaigns, and met Cambridge Analytica at least three times between 2014 and 2015, The New York Times reported.

Wylie told The Guardian: "It didn't make any sense to me ... Why would a Russian oil company want to target information on American voters?"

Kogan, the Cambridge academic, had also been an associate professor at St. Petersburg State University and received Russian government grants while creating and harvesting Facebook data through the app, The Guardian reported.

Curiously, Kogan's work in Russia was never publicized, and colleagues told The Guardian "no one knew" about it. Cambridge University said it was carried out in a "private capacity."

Then in 2019, Facebook got slapped with a massive $5 billion fine by the Federal Trade Commission.

Facebook was hit with a $5 billion fine from the Federal Trade Commission as part of a settlement over claims the company mishandled user data

The fine is a record for the FTC - perhaps a precedent for the kind of punishment that tech giants could expect for mishandling users' data - and is a direct response to the Cambridge Analytica scandal.

Beyond the fine, a series of regulations were imposed on Facebook.

And now, the latest news about the ongoing controversy is that Facebook was aware of Cambridge Analytica far earlier than it previously said.

On Friday, NBC's Dylan Byers published a copy of an internal Facebook message thread from September 2015 where employees talked about "clients in the political space."

One such client, Cambridge Analytica, is described in the correspondence as, "a sketchy (to say the least) data modeling company."

In the emails, employees go into far more detail - and even discuss a potential investigation into data scraping by political partners.

It wasn't until December 2015 when Facebook first realized the extent of Cambridge Analytica's data scraping practices and began to act on it.

In short, Facebook employees were flagging potential concerns related to Cambridge Analytica's practices as early as September 2015, but the company didn't act until March 2018.

More: Facebook Cambridge Analytica Russia Russia investigation

For you

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Facebook understood how dangerous the Trump-linked data firm Cambridge Analytica could be much earlier than it previously said. Here's everything that's happened up until now.

# data[].attributes.attachment_link.description
What did Cambridge Analytica do? Cambridge Analytica harvested information from over 87 million Facebook users through an external app in 2015. The data came from a personality quiz, which around 270,000 people were paid to take. The quiz - "thisisyourdigitallife" - in turn pulled data from their friends' profiles as well, ending in the enormous data stash.

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https://www.straitstimes.com/world/united-states/us-scientists-announce-3d-heart-printing-breakthrough

summary:
scientists have successfully built functional heart parts out of collagen using a 3d bioprinter. it could one day help patients awaiting heart transplants but it will need to be validated through animal testing and eventually human.
tension:
testing on animals and human, which some people consider non-ethical since animals have intrinsic value which must be respected. they feel pain just as humans. However, scientists feel it is necessary since more people who are in need of heart transplant can be saved.

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US scientists announce 3D heart printing breakthrough

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WASHINGTON (AFP) - US scientists have successfully built functional heart parts out of collagen using a 3D bioprinter, a breakthrough they say could one day create entire organs. . Read more at straitstimes.com.