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Satellite Technologies in Emergency Response 

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Satellite Technologies in Emergency Response 

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Humanitarian teams in Turkey and Syria are using machine learning to quickly scope out earthquake damage and strategize rescue efforts

This article is from The Technocrat, MIT Technology Review's weekly tech policy newsletter about power, politics, and Silicon Valley. To receive it in your inbox every Friday, sign up here.
We often hear big (and unrealistic) promises about the potential of AI to solve the world's ills, and I was skeptical when I first learned that AI might be starting to aid disaster response, including following the earthquake that has devastated Turkey and Syria.

But one effort from the US Department of Defense does seem to be effective: xView2. Though it's still in its early phases of deployment, this visual computing project has already helped with disaster logistics and on the ground rescue missions in Turkey.

An open-source project that was sponsored and developed by the Pentagon's Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute in 2019, xView2 has collaborated with many research partners, including Microsoft and the University of California, Berkeley. It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity much faster than is possible with current methods.

Ritwik Gupta, the principal AI scientist at the Defense Innovation Unit and a researcher at Berkeley, tells me this means the program can directly help first responders and recovery experts on the ground quickly get an assessment that can aid in finding survivors and help coordinate reconstruction efforts over time.

In this process, Gupta often works with big international organizations like the US National Guard, the United Nations, and the World Bank. Over the past five years, xView2 has been deployed by the California National Guard and the Australian Geospatial-Intelligence Organisation in response to wildfires, and more recently during recovery efforts after flooding in Nepal, where it helped identify damage created by subsequent landslides.

In Turkey, Gupta says xView2 has been used by at least two different ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group in Adiyaman, Turkey, which has been devastated by the earthquake and where residents have been frustrated by the delayed arrival of search and rescue. xView2 has also been utilized elsewhere in the disaster zone, and was able to successfully help workers on the ground be "able to find areas that were damaged that they were unaware of," he says, noting Turkey's Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform in response to the earthquake.

"If we can save one life, that's a good use of the technology," Gupta tells me.

How AI can help

The algorithms employ a technique similar to object recognition, called "semantic segmentation," which evaluates each individual pixel of an image and its relationship to adjacent pixels to draw conclusions.

Below, you can see snapshots of how this looks on the platform, with satellite images of the damage on the left and the model's assessment on the right-the darker the red, the worse the wreckage. Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes.

This is an improvement over more traditional disaster assessment systems, in which rescue and emergency responders rely on eyewitness reports and calls to identify where help is needed quickly. In some more recent cases, fixed-wing aircrafts like drones have flown over disaster areas with cameras and sensors to provide data reviewed by humans, but this can stilltake days, if not longer. The typical response is further slowed by the fact that different responding organizations often have their own siloed data catalogues, making it challenging to create a standardized, shared picture of which areas need help. xView2 can create a shared map of the affected area in minutes, which helps organizations coordinate and prioritize responses-saving time and lives.

The hurdles

This technology, of course, is far from a cure-all for disaster response. There are several big challenges to xView2 that currently consume much of Gupta's research attention.

First and most important is how reliant the model is on satellite imagery, which delivers clear photos only during the day, when there is no cloud cover, and when a satellite is overhead. The first usable images out of Turkey didn't come until February 9, three days after the first quake. And there are far fewer satellite images taken in remote and less economically developed areas-just across the border in Syria, for example. To address this, Gupta is researching new imaging techniques like synthetic aperture radar, which creates images using microwave pulses rather than light waves.

Second, while the xView2 model is up to 85 or 90% accurate in its precise evaluation of damage and severity, it also can't really spot damage on the sides of buildings, since satellite images have an aerial perspective.

Lastly, Gupta says getting on-the-ground organizations to use and trust an AI solution has been difficult. "First responders are very traditional," he says. "When you start telling them about this fancy AI model, which isn't even on the ground and it's looking at pixels from like 120 miles in space, they're not gonna trust it whatsoever."

What's next

xView2 assists with multiple stages of disaster response, from immediately mapping out damaged areas to evaluating where safe temporary shelter sites could go to scoping longer-term reconstruction. Abbhi, for one, says he hopes xView2 "will be really important in our arsenal of damage assessment tools" at the World Bank moving forward.

Since the code is open source and the program is free, anyone could use it. And Gupta intends to keep it that way. "When companies come in and start saying, We could commercialize this, I hate that," he says. "This should be a public service that's operated for the good of everyone." Gupta is working on a web app so any user can run assessments; currently, organizations reach out to xView2 researchers for the analysis.

Rather than writing off or over-hyping the role that emerging technologies can play in big problems, Gupta says, researchers should focus on the types of AI that can make the biggest humanitarian impact. "How do we shift the focus of AI as a field to these immensely hard problems?" he asks. "[These are], in my opinion, much harder than-for example-generating new text or new images."

What else I'm reading

Teenage girls are not all right. New research from the CDC shows that mental health for high school girls has significantly worsened recently-a crisis experts think has been intensified by social media and the pandemic.

Almost 1 in 3 reported that they seriously considered suicide in 2021, which is up 60% from 2011. Girls fared worse than boys in almost every measure that the CDC tracked, including higher levels of online bullying.

This reminds me of several reports from recent years that show visual social media platforms like Instagram, TikTok, and SnapChat have had an outsize negative impact on how girls deal with an image-obsessed culture.

Last year, I investigated the effects of augmented-reality technologies like face filters on young girls: there are real risks, like the increase of anxiety and challenges to healthy identity formation.

Russia has moved thousands of children out of Ukraine, according to new research based on open-source intelligence (OSINT) from the Humanitarian Research Lab based at the Yale School of Public Health.

The lab's Conflict Observatory project identified the "systematic relocation of at least 6,000 children from Ukraine" to a network of 43 facilities in Russia, including summer camps and adoption centers that appear to conduct "political re-education."

OSINT, the process of gathering publicly accessible information from sources like social media sites and satellite imagery, has been massively important in chronicling war crimes throughout the now year-long conflict. The lab used a combination of firsthand accounts, photographs and information about the camps from the web, and high-resolution satellite imagery to document and research onsite activities.

What I learned this week

Speaking of Russia, I recently learned about an obscure government office called the Main Radio Frequency Center that attempts to control how the country and its occupied areas use the internet. This is the unit that the Kremlin relies on to run its sweeping efforts to censor and surveil digital spaces, and it uses surprisingly manual and blunt tools.

In an investigation published earlier this month, Daniil Belovodyev and Anton Bayev of RadioFreeEurope/RadioLiberty's Russian Investigation Unit reviewed more than 700,000 letters from the unit and 2 million internal documents that were obtained by a Belarusian hacker organization in November 2022. They reveal how the office scours Russian social networks like VK and Odnoklassniki, as well as YouTube and Telegram, to run daily reports on user-generated content and look for signs of internal dissent among Russian citizens (which the center eerily calls "protest moods"). The office has ramped up its efforts since the beginning of the Ukrainian invasion. The Main Radio Frequency Center has invested in bots in an attempt to automate its censorship, but the office also coordinates directly with engineers at web hosting companies and search engines based in Russia, like Yandex, by flagging sites it deems problematic. The investigation reveals just how much effort Russia is putting into its attempt at a great firewall, and how unsophisticated and patchy its tactics can be.

This piece has been updated since it was sent as part of The Technocrat to more clearly reflect xView2's level of precision and the technology's development process.

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How AI can actually be helpful in disaster response

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Humanitarian teams in Turkey and Syria are using machine learning to quickly scope out earthquake damage and strategize rescue efforts

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KELLY KEIRA

nootropics aim to improve brain performance without causing severe negative effects. people want to improve their cognitive wellbeing, so the demand for the drug increases, which resulted in the companies expanding their products containing nootropics. however, their regulation can be limited and excessive usage can cause insomnia, headache, fatigue, dizziness etc. many people are unaware of the true effects of the drug so it is easy to misuse it. theyre frequently active at lower dosages which makes it easier to overdose on it. the usage of the drug is becoming increasingly common among students, around 4 to 7 percent of american college students are ofically diagnosed with ADHD but up to 25% of college students admit to trying stimulants designed for ADHD.

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May and Jia Ying

Ethical Issues:

Inequality especially for those in less developed countries/those who do not have access to proper healthcare

Social pressure where people feel pressured to modify their bodies

Benefits:

drive medical innovation forward

develop new treatments for a variety of medical conditions

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The Ethics of Replacement Body Parts: Is It Ethical to Enhance Our Bodies? – Engineering Replacement Body Parts 2023

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Recently I have been reviewing and watching content regarding our rapid advancements in technology which has given us the ability to replace body parts with prosthetics or other artificial devices. However, with this ability a significant ethical question arises of whether it is ethical to enhance our bodies beyond their natural capabilities. I drew inspiration for this post from the video by the Pew Research Centre included at the end.

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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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Summary: Dr. Josef Mengele conducted horrific experiments on twins and other prisoners in Auschwitz, exploiting them for genetic research. Victims, like Vera Kriegel, endured inhumane conditions, painful injections, and disfigurement. Many died, and if one twin perished, the other was often killed for study. Survivors recall traumatic scenes, such as walls of human eyes. As Soviet forces approached in January 1945, Nazis tried to destroy evidence, but some prisoners were liberated when the camp was freed. These atrocities underscore the need for ethical medical practices and respect for human rights.

Key points

SS Captain Josef Mengele could experiment on an unlimited supply of twins without taking any accountability for their deaths.

Mengele was interested in genetics-more specifically, ‘the inheritance of the propensity to having twins’.

Organs were removed without administering anaesthesia

Pros

Experimenting helps us further understand about a topic and understand new scientific principles and knowledge

Unlimited supply of resources to experiment on

circumvent the long, tedious process of gaining consent from humans to conduct experiments on them and abiding by ethical concerns, accelerating rate of experiments (consequentialist)

Cons

lack of accountability for issuing/ being directly involved in a person’s death

Overrides the moral duty of not ill treating humans (Deontologist)

Possible controversial issues

Risk-benefit analysis

Placebo use

Uninformed consent

Lack of individual right

Lack to privacy

Human dignity

Human test subjects - Nazi Medical experiments’

Article:https://www.bbc.com/news/magazine-30933718

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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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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:

- Biased 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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Marisa Erin Garina Assisted Reproductive Technologies (e.g. In-vitro fertilisation, gestational surrogacy)

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Marisa Erin Garina Assisted Reproductive Technologies (e.g. In-vitro fertilisation, gestational surrogacy)

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Concise Summary:

Singapore is expanding in-vitro fertilisation (IVF) facilities in public hospitals to meet the rising demand from couples seeking medical assistance to conceive. In 2022, approximately 10,500 assisted reproductive technology (ART) treatment cycles were performed, marking a 14% increase from 2020 and an 81% rise since 2013.

The Ministry of Health has announced that KK Women's and Children's Hospital and Singapore General Hospital plan to progressively increase their ART treatment capacities over the next few years. National University Hospital has already expanded its capacity by renovating its IVF laboratory in 2022. There are several factors contributing to the increased demand for IVF services, including greater awareness and acceptance of IVF, as well as couples marrying later in life, which often leads to age-related fertility challenges.

Relevant Ethical Controversies:

Stigma surrounding difficulty to conceive and how IVF reduces the stigma: Greater awareness about IVF treatments -> less stigma surrounding challenges to conceive -> more couples willing to search for IVF treatments

Financial access to IVF: In April, Virtus Fertility Centre Singapore announced the launch of its Ready Baby grant to “lower the cost barrier for couples embarking on their journey to parenthood”, said its managing director Tim Kwan.

The grant gives between $2,000 and $5,000 to eligible couples to help them cover the treatment fees at Virtus, and the centre expects at least 10 to 15 patients to benefit from the grant in its first year.

Storage of frozen eggs and embryos: Issues related to sanctity of life and usage of these embryos as embryos could be used without consent for women

Right to reproduction: Is it fair to "force" pregnancy when its not naturally conceived?

Solutions and Safeguards:

Reducing Stigma Around Infertility

Promote public awareness campaigns to normalize infertility and highlight IVF as one of many valid options.

Emphasize alternative family-building methods, such as adoption, to reduce pressure on biological parenthood.

Improving Financial Access

Expand government subsidies for IVF, especially in private clinics.

Encourage more private initiatives like grants to lower the cost barrier for financially disadvantaged couples.

Regulate IVF pricing to prevent profiteering by clinics.

Preventing Exploitation of Women

Ensure robust informed consent processes to educate women on the physical, emotional, and financial risks of IVF.

Introduce stricter regulations to protect women from coercion or exploitation in egg donation or surrogacy arrangements.

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Hospitals ramp up IVF capacity as more couples in Singapore seek medical help to have a baby

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The number of assisted reproductive treatment cycles has shot up by 81% in the past decade. Read more at straitstimes.com.

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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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ian and ha anh

https://www.nokia.com/networks/case-studies/sendai-city/

after the Great East Japan Earthquake of 2011, Sendai City was devastated. Now, it has established itself as an international leader in disaster risk reduction and reconstruction due to the private wireless drone system in collaboration with Nokia. The network helps first responders alert the public and manage evacuation more effectively.

relevant controversies:

system can be easily hacked;

more developed countries vs less developed countries; accessibility

potential solutions: more developed countries have a duty to help implement this strategy in other countries by collaborating with NGOs (UNDRR) and companies similar to Nokia (deontology)

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Sendai City was devastated by the tsunami that followed the Great East Japan Earthquake of 2011. Since then, the city has established itself as an international leader in disaster risk reduction and reconstruction. As part of a smart city initiative, Sendai wanted to develop an emergency response system that could improve its ability to monitor tsunamis, broadcast alerts and evacuate affected areas.

To build a more effective tsunami response system, the city needed a solution that could:

Provide actionable real-time information about the impact of an earthquake

Sound alarms and deliver instructions to evacuees

Monitor evacuation processes and provide updates to first responders and the public

Operate reliably even if the public communications system fails across the whole city

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Sendai City improves tsunami preparedness with connected drones | Nokia.com

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Nokia and Sendai City have conducted the world's first test of a private wireless-connected drone solution that improves tsunami preparedness and response.

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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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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

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gwenah and kitty

CRISPR, stands for "clustered interspaced short palindromic repeats", is an immune system used by microbes to help protect themselves from invading viruses.

Cell therapy means using CRISPR to make your body' cells attack toxic cells

gene therapy fixes bad genes

PROS:

CRISPR is much easier to program than other tools

can precisely modify a piece od DNA or its chemistry in the body making it a potential tool for clinical uses

CRISPR can be used for diagnostics, manufacturing, sustainability and ecological engineering

cons:

disease prevention and eliminating pesky species, and some definite unethical areas are enhancement and creating designer babies

ethical concerns:

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Smart/Intelligent Ads are not that big of a problem unless you are really concerned about your privacy. Most smart algorithms with the aim of advertising do not collect any extra data than what you are already agreeing to show, like your browsing history which is visible to your network provider. However, if you would like to not receive targetted ads, you could try disabling sensors on an android through developer options. You could also try to generally browse on a browsed without your registered account but at the end of the day, there is no sure way to prevent targettted ads

Pros:Enhanced Personalization: AI enables advertisers to analyze vast amounts of consumer data, facilitating the creation of highly personalized ads that resonate with individual preferences and behaviors.

Improved Efficiency: Automation of tasks such as ad placement, audience targeting, and performance analysis leads to more efficient campaign management, allowing marketers to focus on strategic planning and creative development.

Real-Time Optimization: AI systems can monitor ad performance in real-time and make immediate adjustments to optimize outcomes, ensuring that advertising budgets are utilized effectively.

Cons:

Privacy Concerns: The extensive data collection required for AI-driven personalization raises significant privacy issues, as consumers may be uncomfortable with the level of personal information being utilized.

Risk of Misinterpretation: AI may misinterpret data, leading to inappropriate or irrelevant ad placements that can harm brand reputation.

Ethical Considerations: The use of AI in advertising can lead to ethical dilemmas, such as exploiting consumer vulnerabilities or perpetuating biases present in the data.

Ai is now being implemented in online advertisements. Ai tech is used by big companies like google and meta. Google has already introduces generative AI tools that can replicate work of professional marketing agencies. These algorithms will simply source the advertisers website and materials to generate customised creative assets to target specific audiences. Users can also edit their advertisements feed through Ai chat. Meta also aims to use Ai in their social media apps. However small independent tech advertisement firms can still differentiate themselves by providing more customisable advertisement services

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Council Post: Intelligent Advertising: How Will AI-Powered Ads Reshape The Market?

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As companies actively embrace extended AI capabilities, we are witnessing a shift in the paradigm of what’s possible and what’s not possible for AI in advertising.

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[SAMPLE] Chips monitoring Chinese Students (ABC, 2018)                                 

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[SAMPLE] 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:
- Ensures protection / safety for the child / safety for students at large (especially useful in Covid-19 context- contact tracing)
- Accountability by the school/teachers
- Parents are more assured (child's safety and whereabouts)

Cons & Limitations:
- Lack of privacy
- Restrict freedom (constantly tracked)
- Data leak/ data security
- Are there any health risks?

Possible Controversial issues:
- Privacy vs Security
- The issue of consent (students/parents/school)

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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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Bio-Implants and Enhancements (Andrew and Seen)

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Bio-Implants and Enhancements (Andrew and Seen)

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Neuralink is a company owned by entrepreneur Elon Musk that is building brain implants which contain systems which can "translate thought into action" by connecting human brains to computers

Neuralink is currently still in development but it aims to cure blindness and paraplegia.

Neuralink had recently gotten their first human patient which had shown positive results at its effectiveness

https://builtin.com/hardware/what-is-neuralink

Pros: It is able to act as a way to circumvent life long disabilities like paralysis as it allows signals from the brain to be transferred external and can even potentially allow for the control of robotic limb.

It is also to allow for quicker reaction times for task as it would not require mechanical movements to relay  information.

It allows people to control external robots for rescue missions.

Cons: It is still very untested and could have other unforeseen consequences such as problems after implantation.

It may result in a lost of choice as these implants could potentially be hacked and result in a loss of control of one’s body or choices.

1. Privacy and Data Security*

- Brain Data Exploitation: Neuralink collects neural activity data, raising concerns about how this highly personal information could be stored, shared, or monetized.

- Cybersecurity Risks: Hacking into a Neuralink device could manipulate someone's thoughts, memories, or decisions.

- Government Surveillance: There's concern that such devices could be used for state monitoring of individuals' thoughts and behaviors.

---

### 2. Ethical Dilemmas

- Informed Consent: Questions arise about whether individuals fully understand the risks and implications of implanting a brain device.

- Human Autonomy: If brain implants influence behavior or decision-making, it may reduce personal agency.

- Cognitive Inequality: The technology might be available only to the wealthy, exacerbating socioeconomic divides and creating "enhanced" and "non-enhanced" classes of people.

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### 3. Social and Psychological Impact

- Identity and Free Will: If Neuralink enhances or modifies thought processes, it could blur the line between authentic human experiences and artificial influence.

- Job Displacement: Cognitive enhancements could create an uneven playing field in the job market, potentially rendering those without Neuralink unemployable.

- Cultural Erosion: With rapid integration, traditional human interactions might give way to purely digital or thought-based communication.

---

### 4. Potential for Misuse

- Military Applications: Neuralink could be weaponized for mind control, intelligence gathering, or creating enhanced soldiers.

- Corporate Influence: Companies might exploit Neuralink for targeted advertising or influencing consumer behavior.

- Mind Control: Malicious actors or governments could use the technology to suppress dissent or manipulate populations

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3D bio-printing (human tissues, organs) - yuxuan, jiayi 

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3D bio-printing (human tissues, organs) - yuxuan, jiayi 

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https://www.abc.net.au/science/articles/2015/02/11/4161675.htm

Ethical concerns

1. Healthcare Access and Justice

﻿﻿Raises concerns about treatment availability for different economic groups

﻿﻿Potential to reduce prosthetic and surgical treatment costs through customization

﻿﻿Particularly beneficial for pediatric patients with complex medical needs

2. Safety and Testing

﻿﻿Requires development of new testing models for personalized treatments

﻿﻿Challenges in validating safety for individualized stem cell therapies

- Regulatory bodies must establish new approval standards for emerging technologies

Human Enhancement

Considerations

﻿﻿Potential for extending human physical capabilities beyond natural limits

﻿﻿Raises ethical questions about technological augmentation in military and sports contexts

- Risk of creating technological arms race with potential negative civilian consequences

Solutions:

e.g. using iPSCs instead of embryonic stem cells eliminate the ethical issues as the blastocyst will not have to be damaged

making sure that there are policies in place so that every one has equal probability of gaining access to the 3d printed organs

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Bioethical and Legal Issues in 3D Bioprinting

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Bioethical and legal issues of three-dimensional (3D) bioprinting as the emerging field of biotechnology have not yet been widely discussed among bioethicists around the world, including Russia. The scope of 3D bioprinting includes not only the…