The facial recognition system relies on scraping the data of people from across the web without their explicit consent, a practice which has naturally raised some eyebrows—including the ACLU’s which called it a “nightmare scenario” for privacy.

Around three billion images are said to have been scraped for Clearview AI’s system.

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

Whether you call them protestors or domestic terrorists, the Trump supporters who raided the US Capitol Building last week – incited by the president to halt democracy and overturn the votes of millions of Americans – committed clear criminal offences that were bipartisanly condemned.

In comments to New York Times, Clearview AI CEO Hoan Ton-That claimed the company’s witnesses “a 26 percent increase of searches over our usual weekday search volume” on January 7th, following the riots.

Given the number of individuals involved, law enforcement has a gargantuan task to identify and locate the people that went far beyond exercising their right to peaceful protest and invaded a federal building, caused huge amounts of damage, and threatened elected representatives and staff.

The FBI has issued public appeals, but it’s little surprise that law enforcement is turning to automated means—regardless of the controversy. According to Clearview AI, approximately 2,400 agencies across the US use the company’s facial recognition technology.

Last year, the UK and Australia launched 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.

A similar probe was also launched by the EU’s privacy watchdog. 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 has already been forced to suspend operations in Canada after the federal Office of the Privacy Commissioner of Canada opened an investigation into the company.

While Clearview AI’s facial recognition tech continues to have widespread use in the US, some police departments have taken the independent decision to ban officers from using such systems due to the well-documented inaccuracies which particularly affect minority communities.

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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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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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While it’s been suspected that documented racial bias with facial recognition algorithms has led to false arrests, it’s been difficult to prove.

On Wednesday, the ACLU lodged a complaint against the Detroit police after black male Robert Williams was arrested on his front lawn “as his wife Melissa looked on and as his daughters wept from the trauma”. Williams was held in a “crowded and filthy” cell overnight without being given any reason.

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.

During an interrogation the day after his arrest, the police admitted that “the computer must have gotten it wrong”. Williams was kept incarcerated until after dark “at which point he was released out the front door, on a cold and rainy January night, where he was forced to wait outside on the curb for approximately an hour while his wife scrambled to find child care for the children so that she could come pick him up.”

Speaking to the NY Times, a Detroit police spokesperson said the department “does not make arrests based solely on facial recognition,” and claims witness interviews and a photo lineup were used.

However, a response from the Wayne County prosecutor’s office confirms the department used facial recognition to identify Williams using the security footage and an eyewitness to the crime was not shown the alleged photo lineup.

In its complaint, the ACLU demands that Detroit police end the use of facial recognition “as the facts of Mr. Williams’ case prove both that the technology is flawed and that DPD investigators are not competent in making use of such technology.”

This week, Boston became the latest city to ban facial recognition technology for municipal use. Boston follows an increasing number of cities like San Francisco, Oakland, and California who’ve banned the 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.

“Boston just became the latest major city to stop the use of this extraordinary and toxic surveillance technology. Every other city should follow suit.”

Cases like Mr Williams’ are certainly strengthening such calls. Over 1,000 experts signed an open letter this week against the use of AI for the next chilling step, crime prediction.

(Photo by ev on Unsplash)

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Anyone who has watched the sci-fi classic Minority Report will be concerned about attempts to predict crime before it happens. In an ideal scenario, crime prediction could help determine where to allocate police resources – but the reality will be very different.

The researchers are speaking out ahead of an imminent publication titled ‘A Deep Neural Network Model to Predict Criminality Using Image Processing’. In the paper, the authors claim to be able to predict whether a person will become a criminal based on automated facial recognition.

“By automating the identification of potential threats without bias, our aim is to produce tools for crime prevention, law enforcement, and military applications that are less impacted by implicit biases and emotional responses,” says Harrisburg University Professor and co-author of the paper Nathaniel J.S. Ashby.

“Our next step is finding strategic partners to advance this mission.”

Finding willing partners may prove to be a challenge. Signatories of the open letter include employees working on AI from tech giants including Microsoft, Google, and Facebook.

In their letter, the signatories highlight the many issues of today’s AI technologies which make dabbling in crime prediction so dangerous.

Chief among the concerns is the well-documented racial bias of algorithms. Every current facial recognition system is more accurate when detecting white males and often incorrectly flags members of the BAME community as criminals more often when used in a law enforcement setting.

However, even if the inaccuracies with facial recognition algorithms are addressed, the researchers highlight the problems with the current justice system which have been put in the spotlight in recent weeks following the murder of George Floyd.

In their letter, the researchers explain:

“Research of this nature — and its accompanying claims to accuracy — rest on the assumption that data regarding criminal arrest and conviction can serve as reliable, neutral indicators of underlying criminal activity. Yet these records are far from neutral.

As numerous scholars have demonstrated, historical court and arrest data reflect the policies and practices of the criminal justice system. These data reflect who police choose to arrest, how judges choose to rule, and which people are granted longer or more lenient sentences.

Countless studies have shown that people of color are treated more harshly than similarly situated white people at every stage of the legal system, which results in serious distortions in the data. Thus, any software built within the existing criminal legal framework will inevitably echo those same prejudices and fundamental inaccuracies when it comes to determining if a person has the ‘face of a criminal.’”

Among the co-authors of the disputed paper is Jonathan W. Korn, a Ph.D. student who is highlighted as an NYPD veteran. Korn says that AI which can predict criminality would be “a significant advantage for law enforcement agencies.”

While such a system would make the lives of law enforcement officers easier, it would do so at the cost of privacy and the automation of racial profiling.

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

“The uncritical acceptance of default assumptions inevitably leads to discriminatory design in algorithmic systems, reproducing ideas which normalise social hierarchies and legitimise violence against marginalised groups.”

You can find the full open letter here.

(Photo by Bill Oxford on Unsplash)

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Clearview AI’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.

The EDPB has now ruled that any use of the service by law enforcement in Europe would “likely not be consistent with the EU data protection regime.”

Furthermore, the watchdog “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 scrapes billions of photos from across the internet for its powerful system, a practice which has come under fire by privacy campaigners. “Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland argued recently.

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

“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.

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.

Backlash over Clearview AI forced the company to announce it will no longer offer its services to private companies. The EU’s ruling will limit Clearview AI’s potential customers even further.

Concerns have grown in recent weeks about facial recognition services amid protests over racial discrimination. Facial recognition services have been repeatedly found to falsely flag minorities; stoking fears they’ll lead to automated racial profiling.

IBM and Amazon have both announced this week they’ll no longer provide facial recognition services to law enforcement and have called on Congress to increase regulation to help ensure future deployments meet ethical standards.

(Photo by Christian Lue on Unsplash)

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Met Commissioner Cressida Dick was speaking at the Royal United Services Institute think tank on Monday. Much of Dick’s speech was spent on making the case for British police to use modern technologies to tackle crime.

Dick accused critics of police facial recognition technology as being “highly inaccurate or highly ill-informed.”

Needless to say, this angered said critics who believe Dick is the one who is ill-informed by ignoring an independent report which suggests the technology in question only works in just 19 percent of cases.

“I would say it is for critics to justify to the victims of crimes why police should not be allowed to use tech lawfully and proportionally to catch criminals,” Dick argued.

Dick says she welcomes a public debate about facial recognition but attacked organisations such as Big Brother Watch and Liberty who brought the attention to the wider public.

“It’s unhelpful for the Met to reduce a serious debate on facial recognition to unfounded accusations of ‘fake news’,” Big Brother Watch tweeted. “Dick would do better to acknowledge and engage with the real, serious concerns – including those in the damning independent report that she ignored.”

Liberty tweeted a similar response: “Fact: Met started using facial recognition after ignoring its own review of two-year trial that said its use of the tech didn’t respect human rights. Another fact: scaremongering and deriding criticisms instead of engaging shows how flimsy their basis for using it really is.”

Met Police tests of facial recognition technology so far have 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.

Ironically, the legality of the trials has been called into question. An independent report by Professor Peter Fussey and Dr Daragh Murray last year concluded the six trials they were given access to were probably illegal since they had not accounted for human rights compliance.

Dr Murray said: “This report raises significant concerns regarding the human rights law compliance of the trials.

“The legal basis for the trials was unclear and is unlikely to satisfy the ‘in accordance with the law’ test established by human rights law.

“It does not appear that an effective effort was made to identify human rights harms or to establish the necessity of LFR.

“Ultimately, the impression is that human rights compliance was not built into the Metropolitan Police’s systems from the outset, and was not an integral part of the process.”

You can find a copy of the full report here (PDF)

(Image Credit: Met police helmet by Matt Brown under CC BY 2.0 license)

Interested in hearing industry leaders discuss subjects like this? , , , AI &

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Live-streaming poses a very modern but serious problem. As evident with the white supremacist attack in Christchurch in March, live-streams of terrorism can quickly go viral and taking down videos manually is often a slow process.

Automation can step-in to help identify and remove content to prevent terrorists from achieving the attention they seek. In order to teach its AI how to spot shootings, Facebook is teaming up with police in the UK.

Stephanie McCourt, Facebook’s Law Enforcement Outreach Lead (UK), said:

“Facebook’s work tackling threats from terrorism and extremism never stops. We invest heavily in people and technology to keep people safe on our platforms. But we can’t do it alone.

This partnership with the Met Police will help train our AI systems with the volume of data needed to identify these incidents. And we will remain committed to improving our detection abilities and keeping harmful content off Facebook.”

The police are providing first-person perspective videos from officers’ firearms training to Facebook. The clips will help the social media giant train its algorithms to detect shootings on live-streams.

Not only will early detection enable Facebook to more quickly remove terrorist videos, but it will also help to alert the police faster. The quicker a response can be deployed, the more lives can be potentially saved.

The police became involved with the project through the Counter-Terrorism Internet Referral Unit’s (CTIRU) long-standing relationship with Facebook.

Neil Basu, the UK’s top-ranking counter-terrorism officer, commented:

“As a result of the unit’s relationship with Facebook, coupled with the world-renowned expertise of the Met Police Firearms Command, the Met has been invited to take part in this innovative project.

The technology Facebook is seeking to create could help identify firearms attacks in their early stages and potentially assist police across the world in their response to such incidents.

Technology that automatically stops live streaming of attacks once identified, would also significantly help prevent the glorification of such acts and the promotion of the toxic ideologies that drive them.”

Officers from the world-renowned Firearms Command regularly train to respond to a wide variety of scenarios. These scenarios can include hostage situations carried out on land, water, public transport, and more.

Such a variety of footage will help Facebook train its AI to also recognise a large number of active shooter situations.

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A study commissioned by UK government advisory body the Centre for Data Ethics and Innovation warned that police felt AI may “amplify” prejudices.

50 experts were interviewed by the Royal United Services Institute (RUSI) for the research, including senior police officers.

Racial profiling continues to be a huge problem. More young black men are stopped than young white men. The experts interviewed by the RUSI are worried these human prejudices could make their way into algorithms if they’re trained on existing police data.

It’s also noted how individuals from disadvantaged backgrounds tend to use more public transport. With data likely to be collected from the use of public transport, this increases the likelihood of those individuals being flagged.

The accuracy of facial recognition algorithms has often been questioned. Earlier this year, the Algorithmic Justice League tested all the major technologies and found that algorithms particularly struggled with darker-skinned females.

A similar report published by the American Civil Liberties Union focused on Amazon’s so-called Rekognition facial recognition system. When tested against members of congress, it incorrectly flagged those with darker skin more often.

Both findings show the potentially devastating societal impact if such technology was rolled out publicly today. It’s good to hear British authorities are at least aware of the potential complications.

The RUSI reports that experts in the study want to see clearer guidelines established for acceptable use of the technology. They hope this will provide confidence to police forces to adopt such potentially beneficial new technologies, but in a safe and responsible way.

“For many years police forces have looked to be innovative in their use of technology to protect the public and prevent harm and we continue to explore new approaches to achieve these aims,” Assistant Chief Constable Jonathan Drake told BBC News.

“But our values mean we police by consent, so anytime we use new technology we consult with interested parties to ensure any new tactics are fair, ethical and producing the best results for the public.”

You can find the full results of the RUSI’s study here.

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