Documents obtained by Reuters suggest that, in at least three cases, Google’s researchers were requested to refrain from being critical of AI technology.

A “sensitive topics” review was established by Google earlier this year to catch papers which cast a negative light on AI ahead of their publication.

Google asks its scientists to consult with legal, policy, and public relations teams prior to publishing anything on topics which could be deemed sensitive like sentiment analysis and categorisations of people based on race and/or political affiliation.

The new review means that papers from Google’s expert researchers which raise questions about AI developments may never be published. Reuters says four staff researchers believe Google is interfering with studies into potential technology harms.

Google recently faced scrutiny after firing leading AI ethics researcher Timnit Gebru.

Gebru is considered a pioneer in the field and researched the risks and inequalities found in large language models. She claims to have been fired by Google over an unpublished paper and sending an email critical of the company’s practices.

In an internal email countering Gebru’s claims, Head of Google Research Jeff Dean wrote:

“We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). 

Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted.

A cross-functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why.”

While it’s one word against another, it’s not a great look for Google.

“Advances in technology and the growing complexity of our external environment are increasingly leading to situations where seemingly inoffensive projects raise ethical, reputational, regulatory or legal issues,” Reuters reported one of Google’s documents as saying.

On its public-facing website, Google says that its scientists have “substantial” freedom—but that’s increasingly appearing like it’s not the case.

(Photo by Mitchell Luo on Unsplash)

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Anyone who’s spent much time on the web will know what TL;DR stands for⁠—but, for everyone else, it’s an acronym for “Too Long, Didn’t Read”.

It’s an understandable sentiment we’ve all felt at some point. People lead busy lives. Some outlets now even specialise in short, at-a-glance news.

The problem is, it’s hard to get the full picture of a story in just a brief snippet.

In a world where fake news can be posted and spread like wildfire across social networks – almost completely unchecked – it feels even more dangerous to normalise “news” being delivered in short-form without full context.

There are two sides to most stories, and it’s hard to see how both can be summarised properly.

News folks are not going to love this. Some product manager announced internally a tool that FB is developing called "TL;DR". Basically, it will use AI to summarize long form articles and spit out bullet points so people don't have to read the full piece.

What could go wrong!

— editor Mac 🙃 (@RMac18) December 15, 2020

However, the argument also goes the other way. When articles are too long, people have a natural habit of skim-reading them. Skimming in this way often means people then believe they’re fully informed on a topic… when we know that’s often not the case.

TL;DR needs to strike a healthy balance between summarising the news but not so much that people don’t get enough of the story. Otherwise, it could increase existing societal problems with misinformation, fake news, and lack of media trust.

According to BuzzFeed, Facebook showed off TL;DR during an internal meeting this week. 

Facebook appears to be planning to add an AI-powered assistant to TL;DR which can answer questions about the article. The assistant could help to clear up anything the reader is uncertain about, but it’s also going to have to prove it doesn’t suffer from any biases which arguably all current algorithms suffer from to some extent.

The AI is also going to have to be very careful in not taking things like quotes out-of-context and end up further automating the spread of misinformation.

There’s also going to be a debate over what sources Facebook should use. Should Facebook stick only to the “mainstream media” which many believe follow the agendas of certain powerful moguls? Or serve news from smaller outlets without much historic credibility? The answer probably lies somewhere in the middle, but it’s going to be difficult to get right.

Facebook continues to be a major source of misinformation – in large part driven by algorithms promoting such content – and it’s had little success so far in any news-related efforts. I think most people will be expecting this to be another disaster waiting to happen.

(Image Credit: Mark Zuckerberg by Alessio Jacona under CC BY-SA 2.0 license)

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FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting where burglaries are likely to take place.

“The possibilities seem endless,” writes Michael O’Flaherty, Director of the FRA, in the report’s foreword. “But how can we fully uphold fundamental rights standards when using AI?”

The FRA interviewed over a hundred public administration officials, private company staff, and a diverse range of experts, in a bid to answer that question.

With evidence of algorithms having biases which could lead to automating societal issues like racial profiling—it’s a question that needs answering if the full potential of AI is going to be unlocked for the whole of society.

O’Flaherty says:

“AI is not infallible, it is made by people – and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions. The EU needs to clarify how existing rules apply to AI. And organisations need to assess how their technologies can interfere with people’s rights both in the development and use of AI.

“We have an opportunity to shape AI that not only respects our human and fundamental rights but that also protects and promotes them.”

AI is being used in almost every industry in some form or another—if not already, it will be soon.

Biases in AI are more dangerous in some industries than others. Policing is an obvious example, but in areas like financial services it could mean one person being given a loan or mortgage compared to another.

Without due transparency, these biases could happen without anyone knowing the reasons behind such decisions—it could simply be because someone grew up in a different neighbourhood. Each automated decision has a very real human impact.

The FRA calls for the EU to:

• Make sure that AI respects ALL fundamental rights – AI can affect many rights – not just privacy or data protection. It can also discriminate or impede justice. Any future AI legislation has to consider this and create effective safeguards.
• Guarantee that people can challenge decisions taken by AI – people need to know when AI is used and how it is used, as well as how and where to complain. Organisations using AI need to be able to explain how their systems take decisions.
• Assess AI before and during its use to reduce negative impacts – private and public organisations should carry out assessments of how AI could harm fundamental rights.
• Provide more guidance on data protection rules – the EU should further clarify how data protection rules apply to AI. More clarity is also needed on the implications of automated decision-making and the right to human review when AI is used.
• Assess whether AI discriminates – awareness about the potential for AI to discriminate, and the impact of this, is relatively low. This calls for more research funding to look into the potentially discriminatory effects of AI so Europe can guard against it.
• Create an effective oversight system – the EU should invest in a more ‘joined-up’ system to hold businesses and public administrations accountable when using AI. Authorities need to ensure that oversight bodies have adequate resources and skills to do the job.

The EU has increased its scrutiny of “big tech” companies like Google in recent years over concerns of invasive privacy practices and abusing their market positions. Last week, AI News reported that Google had controversially fired leading AI ethics researcher Timnit Gebru after she criticised her employer in an email.

Google chief executive Sundar Pichai wrote in a memo: “We need to accept responsibility for the fact that a prominent black, female leader with immense talent left Google unhappily.

“It’s incredibly important to me that our black, women, and under-represented Googlers know that we value you and you do belong at Google.”

Gebru gave an interview to the BBC this week in which she called Google and big tech “institutionally racist”. With that in mind, the calls made in the FRA’s report seem especially important to heed.

You can download a full copy of the FRA’s report here.

(Photo by Guillaume Périgois on Unsplash)

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Timnit Gebru is considered a pioneer in the field and researched the risks and inequalities found in large language models.

Gebru claims she was fired by Google over an unpublished paper and sending an email critical of the company’s practices.

The paper questions whether language models can be too big, who benefits from them, and whether they can increase prejudice and inequalities. Some recent cases validate her claims about large models and datasets in general.

For example, MIT was forced to remove a large dataset earlier this year called 80 Million Tiny Images. The dataset is popular for training AIs but was found to contain images labelled with racist, misogynistic, and other unacceptable terms.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset – with sizes of just 32×32 pixels – meant that manual inspection would be almost impossible and couldn’t guarantee all offensive images would be removed.

Gebru reportedly sent an email to the Google Brain Women and Allies listserv that is “inconsistent with the expectations of a Google manager.”

In the email, Gebru expressed her frustration with a perceived lack of progress at Google in hiring women at Google. Gebru claimed she was also told not to publish a piece of research and advised employees to stop filling out diversity paperwork because it didn’t matter.

On top of the questionable reasons for her firing, Gebru says her former colleagues were emailed saying she offered her resignation—which she claims was not the case:

Platformer obtained an email from Jeff Dean, Head of Google Research, which was sent to employees and offers his take on Gebru’s claims:

“We’ve approved dozens of papers that Timnit and/or the other Googlers have authored and then published, but as you know, papers often require changes during the internal review process (or are even deemed unsuitable for submission). Unfortunately, this particular paper was only shared with a day’s notice before its deadline — we require two weeks for this sort of review — and then instead of awaiting reviewer feedback, it was approved for submission and submitted.

A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues.”

Dean goes on to claim Gebru made demands which included revealing the identities of the individuals he and Google Research VP of Engineering Megan Kacholia consulted with as part of the paper’s review. If the demands weren’t met, Gebru reportedly said she would leave the company.

It’s a case of one word against another, but – for a company already in the spotlight from both the public and regulators over questionable practices – being seen to fire an ethics researcher for calling out problems is not going to be good PR.

(Image Credit: Timnit Gebru by Kimberly White/Getty Images for TechCrunch under CC BY 2.0 license)

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The analysis was commissioned by the UK government in October 2018 and will receive a formal response.

Algorithms bring substantial benefits to businesses and individuals able to use them effectively. However, increasing evidence suggests biases are – often unconsciously – making their way into algorithms and creating an uneven playing field.

The CDEI is the UK government’s advisory body on the responsible use of AI and data-driven technology. CDEI has spent the past two years examining the issue of algorithmic bias and how it can be tackled.

Adrian Weller, Board Member for the Centre for Data Ethics and Innovation, said:

“It is vital that we work hard now to get this right as adoption of algorithmic decision-making increases. Government, regulators, and industry need to work together with interdisciplinary experts, stakeholders, and the public to ensure that algorithms are used to promote fairness, not undermine it.

The Centre for Data Ethics and Innovation has today set out a range of measures to help the UK to achieve this, with a focus on enhancing transparency and accountability in decision-making processes that have a significant impact on individuals.

Not only does the report propose a roadmap to tackle the risks, but it highlights the opportunity that good use of data presents to address historical unfairness and avoid new biases in key areas of life.”

The report focuses on four key sectors where algorithmic bias poses the biggest risk: policing, recruitment, financial services, and local government.

Today’s facial recognition algorithms are relatively effective when used on white males, but research has consistently shown how ineffective they are with darker skin colours and females. The error rate is, therefore, higher when facial recognition algorithms are used on some parts of society over others.

In June, Detroit Police chief Editor Craig said facial recognition would misidentify someone around 96 percent of the time—not particularly comforting when they’re being used to perform mass surveillance of protests.

Craig’s comments were made just days after the ACLU (American Civil Liberties Union) lodged a complaint against Detroit Police following the harrowing wrongful arrest of black male Robert Williams due to a facial recognition error.

And that’s just one example of where AI can unfairly impact some parts of society over another.

“Fairness is a highly prized human value,” the report’s preface reads. “Societies in which individuals can flourish need to be held together by practices and institutions that are regarded as fair.”

Ensuring fairness in algorithmic decision-making

Transparency is required for algorithms. In financial services, a business loan or mortgage could be rejected without transparency simply because a person was born in a poor neighbourhood. Job applications could be rejected not on a person’s actual skill but dependent on where they were educated.

Such biases exist in humans and our institutions today, but automating them at scale is a recipe for disaster. Removing bias from algorithms is not an easy task but if achieved would lead to increased fairness by taking human biases out of the equation.

“It is well established that there is a risk that algorithmic systems can lead to biased decisions, with perhaps the largest underlying cause being the encoding of existing human biases into algorithmic systems. But the evidence is far less clear on whether algorithmic decision-making tools carry more or less risk of bias than previous human decision-making processes. Indeed, there are reasons to think that better use of data can have a role in making decisions fairer, if done with appropriate care.

When changing processes that make life-affecting decisions about individuals we should always proceed with caution. It is important to recognise that algorithms cannot do everything. There are some aspects of decision-making where human judgement, including the ability to be sensitive and flexible to the unique circumstances of an individual, will remain crucial.”

The report’s authors examined the aforementioned four key sectors to determine their current “maturity levels” in algorithmic decision-making.

In recruitment, the authors found rapid growth in the use of algorithms to make decisions at all stages. They note that adequate data is being collected to monitor outcomes but found that understanding of how to avoid human biases creeping in is lacking.

“More guidance is needed on how to ensure that these tools do not unintentionally discriminate against groups of people, particularly when trained on historic or current employment data.”

The financial services industry has relied on data to make decisions for longer than arguably any other to determine things like how likely it is an individual can repay a debt.

“Specific groups are historically underrepresented in the financial system, and there is a risk that these historic biases could be entrenched further through algorithmic systems.”

CDEI found limited use of algorithmic decision-making in UK policing but found variance across forces with regards to both usage and managing ethical risks.

“The use of data analytics tools in policing carries significant risk. Without sufficient care, processes can lead to Review into bias in algorithmic decision-making: Executive summary Centre for Data Ethics and Innovation 8 outcomes that are biased against particular groups, or systematically unfair.

In many scenarios where these tools are helpful, there is still an important balance to be struck between automated decision-making and the application of professional judgement and discretion.”

Finally, in local government, CDEI noted an increased use of algorithms to inform decision-making but most are in their early stages of deployment. Such tools can be powerful assets for societal good – like helping to plan where resources should be allocated to maintain vital services – but can also carry significant risks.

“Evidence has shown that certain people are more likely to be overrepresented in data held by local authorities and this can then lead to biases in predictions and interventions.”

The CDEI makes a number of recommendations in its report but among them is:

• Clear and mandatory transparency over how algorithms are used for public decision-making and steps taken to ensure the fair treatment of individuals.
• Full accountability for organisations implementing such technologies.
• Improving the diversity of roles involved with developing and deploying decision-making tools.
• Updating model contracts and framework agreements for public sector procurement to incorporate minimum standards around the ethical use of AI.
• The government working with regulators to provide clear guidance on the collection and use of protected characteristic data in outcome monitoring and decision-making processes. They should then encourage the use of that guidance and data to address current and historic bias in key sectors.
• Ensuring that the Equality and Human Rights Commission has sufficient resources to investigate cases of alleged algorithmic discrimination.

CDEI is overseen by an independent board which is made up of experts from across industry, civil society, academia, and government; it is an advisory body and does not directly set policies. The Department for Digital, Culture, Media & Sport is consulting on whether a statutory status would help the CDEI to deliver its remit as part of the National Data Strategy.

You can find a full copy of the CDEI’s report into tackling algorithmic bias here (PDF)

(Photo by Matt Duncan on Unsplash)

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Moderating a large network like YouTube is no easy task. Aside from the sheer volume of content uploaded every day, moderators are subjected to the worst of humanity and often end up requiring therapy. They’re the unsung heroes.

AI has been hailed as helping to deal with some of the aforementioned issues. Either by automating the moderation process entirely or by offering a helping hand to humans.

Google was left with little choice but to give more power to its AI moderators as the COVID-19 pandemic took hold… but it hasn’t been smooth sailing.

In late August, YouTube said that it had removed 11.4 million videos over the three months prior–the most since the site launched in 2005.

That figure alone should raise a few eyebrows. If a team of humans were removing that many videos, they probably deserve quite the pay rise.

Of course, most of the video removals weren’t done by humans. Many of the videos didn’t even violate the guidelines.

Neal Mohan, chief product officer at YouTube, told the Financial Times:

“One of the decisions we made [at the beginning of the COVID-19 pandemic] when it came to machines who couldn’t be as precise as humans, we were going to err on the side of making sure that our users were protected, even though that might have resulted in [a] slightly higher number of videos coming down.”

Some of the removals left content creators bewildered, angry, and out of pocket in some cases.

Around 320,000 of videos taken down were appealed, and half of the appealed videos were reinstated.

Deciding what content to ultimately remove feels like one of the many tasks which needs human involvement. Humans are much better at detecting nuances and things like sarcasm.

However, the sheer scale of content needing to be moderated also requires an AI to help automate some of that process.

“Over 50 percent of those 11 million videos were removed without a single view by an actual YouTube user and over 80 percent were removed with less than 10 views,” Mohan said. “That’s the power of machines.”

AIs can also help to protect humans from the worst of the content. Content detection systems are being built to automatically blur things like child abuse enough so that human moderators know what it is to remove it—but to limit their psychological impact.

Some believe AIs are better in helping to determine what content should be removed simply using logic rather than a human’s natural biases like their political-leaning, but we know human biases seep into algorithms.

In May, YouTube admitted to deleting messages critical of the Chinese Communist Party (CCP). YouTube later blamed an “error with our enforcement systems” for the mistakes. Senator Josh Hawley even wrote (PDF) to Google CEO Sundar Pichai seeking answers to “troubling reports that your company has resumed its long pattern of censorship at the behest of the Chinese Communist Party.”

Google appears to have quickly realised that replacing humans entirely with AI is rarely a good idea. The company says many of the human moderators who were “put offline” during the pandemic are now coming back.

(Photo by Rachit Tank on Unsplash)

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Clearview AI has repeatedly made headlines, and rarely for good reason. The company’s facial recognition technology is impressive but relies on scraping billions of people’s data from across the web.

“Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland argued recently.

Regulators in the UK and Australia seem to have a different perspective than Ekeland and have announced a joint probe into Clearview AI’s practices.

“The Office of the Australian Information Commissioner (OAIC) and the UK’s Information Commissioner’s Office (ICO) have opened a joint investigation into the personal information handling practices of Clearview Inc., focusing on the company’s use of ‘scraped’ data and biometrics of individuals,” the ICO wrote in a statement.

“The investigation highlights the importance of enforcement cooperation in protecting the personal information of Australian and UK citizens in a globalized data environment.”

A similar probe was launched by the EU’s privacy watchdog last month.

The European Data Protection Board ruled that any use of the service by law enforcement in Europe would “likely not be consistent with the EU data protection regime” and that it “has doubts as to whether any Union or Member State law provides a legal basis for using a service such as the one offered by Clearview AI.”

Clearview AI’s facial recognition system is used by over 2,200 law enforcement agencies around the world. A recent leak suggests it’s even being used by commercial businesses like Best Buy and Macy’s. In May, Clearview said it would stop working with non–law enforcement entities.

The American Civil Liberties Union (ACLU) launched a lawsuit against Clearview AI in May after calling it a “nightmare scenario” for privacy.

Aside from the company’s practices, concerns have been raised about Clearview AI’s extensive ties with the far-right. Ekeland himself has gained notoriety as “The Troll’s Lawyer” for defending clients such as neo-Nazi troll Andrew Auernheimer.

“Companies like Clearview will end privacy as we know it, and must be stopped,” said Nathan Freed Wessler, senior staff attorney with the ACLU’s Speech, Privacy, and Technology Project.

(Photo by The Creative Exchange on Unsplash)

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“If we would use the software only [for subject identification], we would not solve the case 95-97 percent of the time,” Craig said. “If we were just to use the technology by itself to identify someone, I would say 96 percent of the time it would misidentify.”

Craig’s comments arrive just days after the ACLU (American Civil Liberties Union) lodged a complaint against the Detroit police following the harrowing wrongful arrest of black male Robert Williams due to a facial recognition error.

Detroit Police arrested Williams for allegedly stealing five watches valued at $3800 from a store in October 2018. A blurry CCTV image was matched by a facial recognition algorithm to Williams’ driver’s license photo.

Current AI algorithms are known to have a racism issue. Extensive studies have repeatedly shown that facial recognition algorithms are almost 100 percent accurate when used on white males, but have serious problems when it comes to darker skin colours and the fairer sex.

This racism issue was shown again this week after an AI designed to upscale blurry photos, such as those often taken from security cameras, was applied to a variety of people from the BAME communities.

Here’s a particularly famous one:

🤔🤔🤔 pic.twitter.com/LG2cimkCFm

— Chicken3gg (@Chicken3gg) June 20, 2020

And some other examples:

Last week, Boston followed in the footsteps of an increasing number of cities like San Francisco, Oakland, and California in banning facial recognition technology over human rights concerns.

“Facial recognition is inherently dangerous and inherently oppressive. It cannot be reformed or regulated. It must be abolished,” said Evan Greer, deputy director of the digital rights group Fight for the Future.

Over the other side of the pond, facial recognition tests in the UK so far have also been nothing short of a complete failure. An initial trial at the 2016 Notting Hill Carnival led to not a single person being identified. A follow-up trial the following year led to no legitimate matches but 35 false positives.

An independent report into the Met Police’s facial recognition trials, conducted last year by Professor Peter Fussey and Dr Daragh Murray, concluded that it was only verifiably accurate in just 19 percent of cases.

The next chilling step for AI in surveillance is using it to predict crime. Following news of an imminent publication called ‘A Deep Neural Network Model to Predict Criminality Using Image Processing,’ over 1000 experts signed an open letter last week opposing the use of AI for such purposes.

“Machine learning programs are not neutral; research agendas and the data sets they work with often inherit dominant cultural beliefs about the world,” warned the letter’s authors.

The acknowledgement from Detroit’s police chief that current facial recognition technologies do not work in around 96 percent of cases should be reason enough to halt its use, especially for law enforcement, at least until serious improvements are made.

(Photo by Joshua Hoehne on Unsplash)

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PAI is a US-led alliance which aims to foster the ethical development and deployment of AI technologies. Baidu was the only Chinese member.

The loss of Baidu’s expertise and any representation from China is devastating for PAI. Ethical AI development requires global cooperation to set acceptable standards which help to ensure safety while not limiting innovation.

Baidu has officially cited financial pressures for its decision to exit the alliance.

In a statement, Baidu wrote:

“Baidu shares the vision of the Partnership on AI and is committed to promoting the ethical development of AI technologies. 

We are in discussions about renewing our membership, and remain open to other opportunities to collaborate with industry peers on advancing AI.”

Directors from PAI hope to see Baidu renew its membership to the alliance next year.

Cooperation between American and Chinese firms

Cooperation between American and Chinese firms is getting more difficult as the world’s largest economies continue to implement sanctions on each other.

The US has criticised China for its handling of the coronavirus outbreak, trade practices, its mass imprisonment and alleged torture of Uyghur Muslims in “re-education” camps, and breaking the semi-autonomy of Hong Kong.

In the tech world, much of the focus has been on Chinese telecoms giant Huawei – which the US accuses of being a national security threat. Canada arrested Huawei CFO Meng Wanzhou last year on allegations of using the company’s subsidiaries to flout US sanctions against Iran. Two Canadian businessmen that were arrested in China shortly after Meng’s detention, in a suspected retaliation, were charged with spying by Beijing this week.

An increasing number of Chinese companies, including Huawei, have found themselves being added to an ‘Entity List’ in the US which bans American companies from working with them without explicit permission from the government.

The US added six Chinese AI companies to its Entity List last October, citing their role in alleged human rights violations.

Earlier this week, the US Commerce Department made an exception to Huawei’s inclusion on the Entity List which allows US companies to work with the Chinese giant for the purposes of developing 5G standards. Hopefully, we can see the same being done for AI companies.

However, on the whole, cooperation between American and Chinese firms is getting more difficult as a result of the political climate. It wouldn’t be surprising to see more cases of companies like Baidu dropping out of well-intentioned alliances such as PAI if sensible resolutions to differences are not sought.

(Photo by Erwan Hesry on Unsplash)

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“Companies like Clearview will end privacy as we know it, and must be stopped,” said Nathan Freed Wessler, senior staff attorney with the ACLU’s Speech, Privacy, and Technology Project.

“The ACLU is taking its fight to defend privacy rights against the growing threat of this unregulated surveillance technology to the courts, even as we double down on our work in legislatures and city councils nationwide.”

Clearview AI has repeatedly come under fire due to its practice of scraping billions of photos from across the internet and storing them in a database for powerful facial recognition services.

“Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland said recently.

The company’s facial recognition system is used by over 2,200 law enforcement agencies around the world – and even commercial businesses like Best Buy and Macy’s, according to a recent leak.

In a press release, the ACLU wrote:

“The New York Times revealed the company was secretly capturing untold numbers of biometric identifiers for purposes of surveillance and tracking, without notice to the individuals affected.

The company’s actions embodied the nightmare scenario privacy advocates long warned of, and accomplished what many companies — such as Google — refused to try due to ethical concerns.”

However, even more concerning is Clearview AI’s extensive ties with the far-right.

Clearview AI founder Hoan Ton-That claims to have since disassociated from far-right views, movements, and individuals. Ekeland, meanwhile, has gained notoriety as “The Troll’s Lawyer” for defending clients such as neo-Nazi troll Andrew Auernheimer.

The ACLU says its lawsuit represents the first “to force any face recognition surveillance company to answer directly to groups representing survivors of domestic violence and sexual assault, undocumented immigrants, and other vulnerable communities uniquely harmed by face recognition surveillance.”

Facial recognition technologies have become a key focus for the ACLU.

Back in March, AI News reported the ACLU was suing the US government for blocking a probe into the use of facial recognition technology at airports. In 2018, the union caught our attention for highlighting the inaccuracy of Amazon’s facial recognition algorithm – especially when identifying people of colour and females.

“Clearview’s actions represent one of the largest threats to personal privacy by a private company our country has faced,” said Jay Edelson of Edelson PC, lead counsel handling this case on a pro bono basis.

“If a well-funded, politically connected company can simply amass information to track all of us, we are living in a different America.”

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