# data[].attributes.attachment_link.readable_text
No existing law requires federal law enforcement personnel to take training before using face recognition or to follow particular standards when using face recognition in a criminal investigation.

The DOJ plans to issue a department-wide civil rights and civil liberties policy for face recognition but has yet to set a date for planned implementation, according to the report. It says that DOJ officials, at one point in 2022, considered updating its policy to allow a face recognition match alone to justify applying for a search warrant.

The commercial face recognition tools used by the FBI and other federal agencies attempt to match a photo of a suspect or victim to images of faces in databases that can contain millions of images. After the software offers up a list of possible matches, humans decide whom to subject to further investigation.

Face recognition models made by commercial vendors in the US have misidentified Asian Americans and women with dark skin at higher rates than the rest of the population, according to a National Institute of Standards and Technology report. Though government assessments of face recognition continue to show improvements in identifying people across different demographic groups, false arrests continue to take place, often the results of a combination of faulty technology and poor investigative work by police officers, and almost exclusively involving Black men. Last month, also, news broke about Porcha Woodruff, who was falsely arrested despite the fact she was visibly pregnant, unlike a suspect seen in security camera footage.

Misidentification by a witness or law enforcement officer can also result in a false arrest. Research shows that people are generally bad at recognizing other people they don't know and especially bad at recognizing people of a different race. Photo quality and the time difference between a probe photo and a photo in a database can also influence outcomes.

The FBI has been under pressure from government and lawmakers to better protect the rights of US residents against the power of face recognition for years. The GAO began calling for the FBI to assess the accuracy and privacy implications of its in-house face recognition software in 2016. It renewed those calls in 2019, when bipartisan lawmakers also pressured the FBI to add safeguards.

In 2022, a congressional committee instructed the Department of Justice to create an ethical-use-of-face-recognition policy, but the agency has yet to put such a policy into practice. That same year, President Joe Biden signed an executive order directing the department to commission a National Academy of Sciences study of face recognition's impact on privacy, civil rights, and civil liberties. It also required the Department of Justice engage in an interagency process to develop best practices for face recognition use by law enforcement agencies.

Sneha Revanur, founder of Encode Justice, a youth nonprofit that wants a moratorium on face recognition use by law enforcement, says the technology can be overlooked amid recent excitement and fear about generative AI such as ChatGPT. "It's critical that we don't leave behind unfinished business around issues like face recognition," she says. Revanur says a federal moratorium on the technology would provide time to study what kinds of training and use policies could possibly lessen the power of a surveillance tool that can violate civil rights.

# data[].attributes.attachment_link.title
FBI Agents Are Using Face Recognition Without Proper Training

# data[].attributes.attachment_link.description
The FBI makes heavy use of face recognition services like that of controversial startup Clearview AI, but 95 percent of the agents using them haven't completed training on the technology.

# data[].attributes.body
The FBI uses facial recognition algorithms to search for suspects of crime. This software is provided by third parties. This software is then used by the FACE services unit, which compares the faces of suspects to the faces from government databases. FACE stands for Facial Analysis, Comparison, and Evaluation. The unit then filters through potential matches manually to quickly narrow down the search for the identity of the suspect. According to the FBI, this process only provides "most likely candidates", and not positive identification.

The Unit has performed 390,186 searches on different databases to support FBI investigations.

# data[].attributes.attachment_link.description
This process is automatic. Your browser will redirect to your requested content shortly.

# data[].attributes.attachment_link.readable_text
While much of the public might believe that facial-recognition software is the stuff of Jason Bourne films, the reality is that the technology is already being used by government agencies such as the F.B.I. With new technologies come new ways for the government to ostensibly fight crime-an effort that often includes major privacy trade-offs, and, when not used responsibly, can put residents and civilians at risk.

Such is the case with FACE, an F.B.I. program described in a new, blistering report from the United States Government Accountability Office. FACE stands for Facial Analysis, Comparison, and Evaluation, the name of a relatively new unit within the agency. The G.A.O. found that the F.B.I. has been disregarding some of even the most basic privacy protections and standards.

To wit: the driver's-license photos of the residents of 16 states and some additional 30 million photos from a biometric database are available for the F.B.I. to search at will. Another 18 states are reportedly negotiating with the F.B.I. over the use of driver's-license images. The F.B.I. hadn't sufficiently notified the public of the technology's use.

"Not knowing how often the system is producing false positives makes it impossible to know if FACE actually benefits F.B.I. investigations."

According to the G.A.O., the F.B.I. has no idea how often the technology they're using mis-identifies people. It has previously been determined that facial-recognition software can exhibit a racial bias-some algorithms are significantly more effective at parsing the faces of white people than they are at identifying black people. Erroneous identification in an F.B.I. investigation can, of course, wreak havoc on an individual's life.

"We have a mismatch here between the power and speed with which these technologies are being developed, and the oversight that they're being subject to," Jay Stanley, a senior policy analyst at the A.C.L.U.'s Speech, Privacy, and Technology Project told VF.com.

Not knowing how often the system is producing false positives also makes it impossible to know if FACE actually benefits F.B.I. investigations at all, the G.A.O. found. If that sounds familiar, perhaps that's because it recalls the government's eventual admission that N.S.A. mass-surveillance programs revealed by Edward Snowden did not disrupt a single plot that would not already have been revealed through other, less intrusive forms of targeted surveillance.

The F.B.I. also has not been auditing FACE to determine whether it complies with standard privacy rules and protections, and, as the A.C.L.U. notes, "for five years did not publish a legally required System of Records Notice (SORN) informing the public about its use of face recognition."

How often was the F.B.I. using FACE? According to the G.A.O., the unit fielded approximately 214,920 searches or requests between 2011 and 2015-36,420 involving the 16 states' driver's-license photos. Overall, FACE found 8,590 cases in which a "likely candidate" was returned to an F.B.I. agent.

According to Alvaro Bedoya, the executive director of the Center on Privacy and Technology at Georgetown University Law Center, "facial recognition searches are far more common than wiretaps."

"And yet," Bedoya tweeted on Thursday, "we regulate wiretaps, and don't regulate face recognition."

"Erroneous identification can, of course, wreak havoc on an individual's life."

In response to a draft version of the G.A.O.'s report, the Department of Justice (the parent agency of the F.B.I.) said it concurred with some, but not all, of the report's findings and recommendations. The D.O.J. claimed that since the FACE program is meant to provide potential leads, rather than "positive identification," even inaccurate leads do not amount to false positives. The G.A.O. disagreed, as it also did when the D.O.J. claimed that, since it lacks the authority to set a certain accuracy benchmark for facial-recognition software used by external agencies, there therefore cannot be a standard of any sort.

The G.A.O. pointed out that the technology used by the F.B.I. and its state and local partners can search some 380 million photos. If an agency is absorbing the biometric data of millions of unwitting Americans, the G.A.O. argues, it should greatly increase efforts to inform the public, protect its data from leaks, and address concerns about the technology's potential inaccuracy.

# data[].attributes.attachment_link.title
The F.B.I.'s Facial-Recognition Program Is a Scary Mess

# data[].attributes.attachment_link.description
A new oversight report finds the agency's powerful, sci-fi-like technology is not being subject to even the most basic privacy protections.

# data[].attributes.body
Although the FBI claims they only use "government data" such as mug shots, they fail to mention their full access to driver's license photos in 16 states -- giving the FBI access to hundreds of thousands of adults' faces in addition to their 30 million photos from a biometric database. The issue is that the government does not notify the population about this. From the Libertarian ethical perspective, this is a massive invasion of personal freedoms. Unless every person who has their face in the government database has agreed to it, the government has no right to keep track of them, as that is not the purpose of a government from the libertarian standpoint.

# data[].attributes.body
The F.B.I. has no system to find how often the facial recognition algorithm identifies a false positive. There is even a concern that the algorithm, depending on how it is trained, can have a racial bias, identifying white subjects more accurately than black subjects. In a country in which African-American incarceration rates are much higher than other racial groups, this issue can be extremely problematic. Porcha Woodruff, a Black pregnant mother, was arrested this year for robbery in Detroit. It was the first known false positive arrest due to facial recognition. From the Bentham Utilitarian perspective, this is OK, because only a small minority will be negatively affected, and the majority will benefit from reduced crime.

# data[].attributes.body
https://www.theatlantic.com/technology/archive/2016/04/the-underlying-bias-of-facial-recognition-systems/476991/

# data[].attributes.attachment_link.readable_text
Depending on how algorithms are trained, they could be significantly more accurate when identifying white faces than African American ones.

In 16 "undisclosed locations" across northern Los Angeles, digital eyes watch the public. These aren't ordinary police-surveillance cameras; these cameras are looking at your face. Using facial-recognition software, the cameras can recognize individuals from up to 600 feet away. The faces they collect are then compared, in real-time, against "hot lists" of people suspected of gang activity or having an open arrest warrant.

Considering arrest and incarceration rates across L.A., chances are high that those hot lists disproportionately implicate African Americans. And recent research suggests that the algorithms behind facial-recognition technology may perform worse on precisely this demographic. Facial-recognition systems are more likely either to misidentify or fail to identify African Americans than other races, errors that could result in innocent citizens being marked as suspects in crimes. And though this technology is being rolled out by law enforcement across the country, little is being done to explore-or correct-for the bias.

State and local police began using facial recognition in the early 2000s. The early systems were notoriously unreliable, but today law-enforcement agencies in Chicago, Dallas, West Virginia, and elsewhere have acquired or are actively considering more sophisticated surveillance camera systems. Some of these systems can capture the faces of passersby and identify them in real-time. Sheriff's departments across Florida and Southern California have been outfitted with smartphone or tablet facial recognition systems that can be used to run drivers and pedestrians against mug shot databases. In fact, Florida and several other states enroll every driver's license photo in their facial recognition databases. Now, with the click of a button, many police departments can identify a suspect caught committing a crime on camera, verify the identity of a driver who does not produce a license, or search a state driver's license database for suspected fugitives.
The limited testing that has been done on these systems has uncovered a pattern of racial bias.
But as with any emerging technology, facial recognition is far from perfect. Companies market facial recognition technology as " a highly efficient and accurate tool" with " an identification rate above 95 percent." In reality, these claims are almost impossible to verify. The facial-recognition algorithms used by police are not required to undergo public or independent testing to determine accuracy or check for bias before being deployed on everyday citizens. More worrying still, the limited testing that has been done on these systems has uncovered a pattern of racial bias.

The National Institute of Standards and Technologies (NIST) conducts voluntary tests of facial-recognition vendors every four years. In 2010, NIST observed that accuracy rates had improved tenfold between each round of testing, a dramatic testament to the technology's rapid advances.

But research suggests that the improving accuracy rates are not distributed equally. To the contrary, many algorithms display troubling differences in accuracy across race, gender, and other demographics. A 2011 study, co-authored by one of the organizers of NIST's vendor tests, found that algorithms developed in China, Japan, and South Korea recognized East Asian faces far more readily than Caucasians. The reverse was true for algorithms developed in France, Germany, and the United States, which were significantly better at recognizing Caucasian facial characteristics. This suggests that the conditions in which an algorithm is created-particularly the racial makeup of its development team and test photo databases-can influence the accuracy of its results.

Similarly, a study conducted in 2012 that used a collection of mug shots from Pinellas County, Florida to test the algorithms of three commercial vendors also uncovered evidence of racial bias. Among the companies evaluated was Cognitec, whose algorithms are used by police in California, Maryland, Pennsylvania, and elsewhere. The study, co-authored by a senior FBI technologist, found that all three algorithms consistently performed 5-to-10 percent worse on African Americans than on Caucasians. One algorithm, which failed to identify the right person in 1 out of 10 encounters with Caucasian subjects, failed nearly twice as often when the photo was of an African American.
The closest match is treated as a lead, and police begin investigating an innocent person.
This bias is particularly unsettling in the context of the vast racial disparities that already exist in police traffic stop, stop and frisk, and arrest rates across the country. African Americans are at least twice as likely to be arrested as members of any other race in the United States and, by some estimates, up to 2.5 times more likely to be targeted by police surveillance. This overrepresentation in both mug shot databases and surveillance photos will compound the impact of that 5-to-10 percent difference in accuracy rates. In other words, not only are African Americans more likely to be misidentified by a facial-recognition system, they're also more likely to be enrolled in those systems and be subject to their processing.

Imagine police are investigating a robbery that was caught on camera. When they run a video still of the suspect's face against their facial-recognition database, they receive 10 possible matches, but none are a perfect match to the suspect. Nonetheless, the closest match is treated as a lead, and police begin investigating an innocent person. Thanks to the accuracy-rate bias in facial-recognition algorithms today, this scenario is statistically more likely to happen to an African American than a white person.

This is not to say that facial-recognition algorithms are "racist," or that racial bias has been intentionally introduced into how they operate. Rather these demonstrated disparities may be introduced unintentionally at a number of points in the process of designing and deploying a facial recognition system. The engineer that develops an algorithm may program it to focus on facial features that are more easily distinguishable in some races than in others-the shape of a person's eyes, the width of the nose, the size of the mouth or chin. This decision, in turn, might be based on preexisting biological research about face identification and past practices which themselves may contain bias. Or the engineer may rely on his or her own experience in distinguishing between faces-a process that is influenced by the engineer's own race.
An algorithm trained exclusively on either African American or Caucasian faces recognized members of the race in its training set more readily than members of any other race.
In addition, algorithms learn how to calculate the similarity of photos by practicing on pre-existing training sets of faces. So even if the features on which an algorithm focuses are race-neutral, a training set of images that contains disproportionate numbers of one race will bias the algorithm's accuracy rates in that direction. The 2012 study on mug shots found that an algorithm trained exclusively on either African American or Caucasian faces recognized members of the race in its training set more readily than members of any other race.

Unfortunately, the two studies discussed here are among the only works on racial bias published in the past decade-far too little review for a technology with the power to implicate people as suspects in a criminal investigation. NIST is well placed to lead the development of a comprehensive set of bias tests that could, alongside its existing regime, form the basis of a formal certification framework for facial recognition systems. But NIST testing of facial recognition systems is voluntary. Law-enforcement agencies-or the city councils and state legislatures that pay their bills-are well-placed to require that facial recognition software vendors submit to NIST's existing accuracy tests, and any new tests that it develops.

Until those requirements are put in place, facial-recognition vendors should voluntarily submit their algorithms to NIST's existing testing regime and other public, peer-reviewed research to measure-and begin to correct-the racial bias in their algorithms.

In the meantime, we're conducting an in-depth study on facial-recognition use by state and local law-enforcement agencies across the country. Our research, based on responses to Freedom of Information requests sent to more than 100 departments, aims to develop a clear picture of what these systems look like, how and on whom they are used, and what policies and legal standards-if any-are in place to constrain their use. The final report, to be released this summer, will include recommendations for government at the federal, state, and local level, the law enforcement agencies themselves, companies, and advocates on how to ensure this technology used in a manner consistent with privacy and civil liberty interests of all citizens, regardless of race.

# data[].attributes.attachment_link.title
Facial-Recognition Software Might Have a Racial Bias Problem

# data[].attributes.attachment_link.description
Depending on how algorithms are trained, they could be significantly more accurate when identifying white faces than African American ones.

# data[].attributes.body
Unlike human criminal investigators, a facial recognition algorithm can parse through billions of faces in minutes. Facial recognition helped arrest the January 6th insurrectionists, with great efficiency, as there was lots of video data of the faces of participants. This aspect of speed is also critical for cases involving kidnappings, where a manual investigation might just be too slow. From a utilitarian standpoint, this tech is good, as it only does a human investigator's job, but much quicker. Therefore, there is a large increase in safety, and thus happiness.

# data[].attributes.body
China in recent years has abused the power of AI and facial recognition to further the authoritarian regime's agenda. Although they claim to use it to improve society, they have gone as far as to have people shunned for minor criminal offenses on billboards using the technology. Behind the scenes, they use biometric data and behavior data along with AI to identify citizens who have the potential to threaten the state. Those who are flagged are arrested and punished. Crimes include being Muslim.

# data[].attributes.body
https://ideasforpeace.org/content/human-rights-in-ai-facial-recognition/

# data[].attributes.attachment_link.readable_text
Author: Caroline Adams

AI Facial Recognition

AI Facial Recognition has become one of the most controversial topics in the digital era. It is a successful tool and measure in biometrical technological advances. However, it has ethical barriers and dangers to human rights, especially the right to privacy. As this technology has been used to track criminals or anyone that may be a harm to society, it is an invasion of privacy. One of the most detrimental uses of this technology is China's use against the Uyghur community.

The Uyghur Crisis in China has become one of the most alarming atrocities since the Second World War (Lu, 2021). As millions of Uyghurs experience some of the highest degrees of human rights violations, what becomes the most problematic is the lack of coverage, accountability, and policy enforcement. The voices in the Uyghur community have been silenced and oppressed by the Chinese government. AI facial recognition technology has tremendous power behind it. The international community must continue its commitment to human rights and orchestrate the use of AI to enhance those rights and not weaken them.

Facial recognition technology is a system built on algorithms that function to identify people through imaging or video. Although this is not a recent technology, it has dramatically advanced in recent years. In its advancement, artificial intelligence (AI) has become one of the most controversial technologies. AI systems can search for multiple faces making comparisons to one another or detecting multiple faces in one area.

AI technology operates in three steps: it captures a photo or video, then it measures "landmarks" or facial features, and then the information is converted into a mathematical formula in a database that can compare those features with others in the system (EconomistMagazine, 2018). The accuracy holds at a 99.5% accuracy rate. AI face recognition software does have advantages including real-time identification, anti-spoofing measures, lessened racial or gender bias model training across millions of faces, and can be used across multiple cameras. AI is used in health care, security, airport boarding, and proctoring ( Ai Facial Recognition Technology Overview 2021 2021).

There is a gray area in the legality and ethical usage of AI facial recognition. The majority of the images that are collected are done so without permission. In 2016, Seattle's University of Washington collected 3.3 million photos of faces from Flickr without consent and posted them to a database. "Currently, there are no clear legal safeguards regarding the gathering of facial recognition training data - but, recently, Facebook paid a $650 million settlement for harvesting facial data" ( Ai Facial Recognition Technology Overview 2021).

Companies have come forward claiming to take more responsibility with facial recognition technology, such as not reinforcing existing biases, not violating internationally accepted ethical norms, and protecting privacy with control and transparency ( Ai Facial Recognition Technology Overview 2021 2021). As companies and governments have acted in the explosive growth of artificial intelligence, the global market is expected to reach $12.92 billion by 2027. This technology has stepped outside the boundaries of security and safety, it has entered into retail, transportation, hospitality, and banking (EconomistMagazine, 2018).

The conversation for this technology internationally has varied and become incredibly controversial. Figures like Elon Musk and Stephen Hawking have publicly voiced their hesitancy on AI technology as they view it could destroy humanity. Whereas China, the leading country in the industry, uses it daily through consumer shopping, airport check-in, and banking. YITU is innovating a 'smart city' where the blueprint is built on AI facial recognition. It could reach into reading people's emotions to sexuality. The Chinese government has plans to monitor and control citizens through an authoritarian lens using this technology. Currently, China has 170 million CCTV cameras, and it is estimated they will have 400 million in the next 3 years.

As China claims this technology will improve society, others see this as incredibly dangerous for human rights. China has developed the capacity for AI to be abused by the authoritarian government including having their citizens named and shammed for minor offenses on billboards using AI facial recognition. The ethnic minorities are most threatened, especially the Uighur community. There are at least 1 million Uighur detained in secret camps where abuse is prevalent, the largest incarceration of a minority group in the world today (EconomistMagazine, 2018). "China's goal is to establish industrial standards now, so that they can have a hand in shaping the development and implementation of worldwide standards" ( Ai Facial Recognition Technology Overview 2021). A technology that is so powerful needs to be deeply ethically questioned.

China's Surveillance and Security

The lockdown and authoritarian surveillance in China have hit dramatic levels that infringe on the human rights of the Uyghurs. Through financial, human, and technical investments, China has increased security with police checkpoints and police stations. Monitoring the region includes looking at their familial and social networks, behavior, beliefs, and relations to other family members. "Xinjiang authorities conduct compulsory mass collection of biometric data, such as voice samples and DNA, and use artificial intelligence and big data to identify, profile, and track everyone in Xinjiang" (HRW, 2020). Through this high-level technology, the government can weed out those that have behaviors that are potentially threatening the State. Those labeled as threatening to the State have been detained or punished with harsh treatment, in justification of eliminating "problematic ideas" of Turkic Islam.

China has built algorithms used to track the Uyghur community and has installed systems for advanced facial recognition. This system allows for racial profiling of the Uyghurs, where the cameras pick up their appearance and keep recognition records. "China a pioneer in applying next-generation technology to watch its people, potentially ushering in a new era of automated racism" (Mozur, 2018). The authorities have sustained a "surveillance net" that collects and stores people's DNA to track the Uyghurs. The technology has reached outside of the Xinjiang region, and it has launched into cities like Hangzhou and Wenzhou.

Since Uyghurs have a distinct look compared to the majority Han population, the technology has successfully monitored and controlled the community. CloudWalk, a Chinese start-up for surveillance systems, listed directly on the website "if originally one Uighur life in a neighborhood, and within 20 days six Uighurs appear, it immediately sends an alarm to law enforcement" (Mozur, 2018). In combination with the racist laws in the name of counterterrorism, this advanced technology has given provision for unsafe environments inside and outside of the camps. It has created a new era of racial profiling resulting in one of the worst genocides we may ever witness.

China's use of AI facial recognition technology has outlined a large spectrum of ethical boundaries. The XUAR (Xinjiang Uyghur Autonomous Region) has been turned into a surveillance state, enforced by Chen Quanguo Party Secretary. Like his efforts in Tibet, he has begun to use a "massive system of electronic surveillance, which included an extensive database on Uyghur residents' habits, relations, religiosity, and other traits that are used to assess their 'loyalty to the state" (Roberts, 2022). This suspicion has increased for decades, resulting in the creation of incarceration camps in 2017, and by 2018, the camps contained 1 to 2 million Uyghurs and other Muslim minorities. The PRC enforces a cultural genocide through policies that demolish the community's identity and restructure its landscape.

XI Jinping has used the Mao-era belief system to orchestrate political control where technology has supported this power (Robert, 2022). The United Nations Human High Commissioner for Human Rights, Michele Bachelet, declared a moratorium on the sale and use of artificial intelligence systems until the "negative, even catastrophic" risk can be properly advised. Her response was after reviewing the UN report, Urgent action needed over artificial intelligence risk to human rights, in reflection of Pegasus spyware that enters into smartphones and gains access to all information. "The Pegasus revelations were no surprise to many people, Ms. Bachelet told the Council of Europe's Committee on Legal Affairs and Human Rights, about the widespread use of spyware commercialized by the NSO group, which affected thousands of people in 45 countries across four continents" (United Nations , 2021). The concern for the right to privacy has a huge impact on the use of this technology.

The United Nations Human Rights Office of High Commissioner developed a report that reviewed and analyzed the effects of AI technology on the right to privacy, the rights to health, education, freedom of movement, peaceful assembly, association, and freedom of expression. Tim Engelhardt, Human Rights Officer of Law and Democracy Section, has stepped forward with high concern for AI technology, stating "the European Union's agreement to strengthen the rules on control" and "the growth of international voluntary commitments and accountability mechanisms", he warned that "we don't think we will have a solution in the coming year, but the first steps need to be taken now or many people in the world will pay a high price" (Neuman, 2021). This report suggested that states and businesses do not carry out due diligence when installing this software and uncovered the mistreatment of numerous counts for people, such as the denial of social security benefits due to faulty AI, or the unjust arrest because of flawed technology. Most importantly, the discriminatory risk in technology is most detrimental to human rights (Neuman, 2021).

As the responsibility and accountability rest in the state and governance, public and private technology companies have a huge role in eliminating human rights violations with the use of their products. In 2020, MIT cut off its relationship with iFlytek, a Chinese artificial intelligence company that supplied the technology that surveilled Muslims in Xinjiang. Maria Zuber, the Vice President of research at MIT, stated "We take very seriously concerns about national security and economic security threats from China and other countries and human rights issues" (Knight, 2020). In 2019, the US government banned Chinese AI companies from doing business with American companies because of their use of technology to oppress the Muslim Minority. The Human Rights Watch declared that iFlytek supplies the Xinjiang authorities with technology that was used to identify the Muslim Community with voiceprints. The surveillance has resulted in the detention and disappearance of 1 million Uighurs (Knight, 2020).

Ethical Framework

To highlight the beneficial effects of China's use in AI facial recognition, there has been significant success in eliminating crime. For example, in Zhengzhou, a police officer was able to spot a heroin smuggler at a train station. In Qingdao, artificial intelligence captured two dozen criminal suspects during a festival. These programs have found criminals, like murderers, drug dealers, etc. Beijing is embracing the technology that would track 1.4 billion people and assemble national surveillance systems. Train stations can now scan for the most wanted criminals in China (Mozur, 2018). "Already, China has an estimated 200 million surveillance cameras - four times as many as the United States" (Mozur, 2018).

One of the major necessities in the usage of AI technology is transparency by companies, States, and international organizations. The world must be able to fully grasp the development, usage, and intentions behind the technology. The OHCHR report states that: "The complexity of the data environment, algorithms and models underlying the development and operation of AI systems, as well as intentional secrecy of government and private actors are factors undermining meaningful ways for the public to understand the effects of AI systems on human rights and society" (United Nations, 2021). There is responsibility within the public and private sector, technology companies, academic research, State obligations, international obligations, and NGO commitments to use facial recognition to enhance human rights.

Amnesty International launched a global campaign to ban facial recognition technology as it allows for racist policing and threatens the right to protest. Matt Mahmoudi, AI and Human Rights Researcher at Amnesty International stated, "Facial recognition risks being weaponized by law enforcement against marginalized communities around the world. From New Delhi to New York, this invasive technology turns our identities against us and undermines human rights" . Matt and the organization feel the technology can function as racial weaponry. UK courts ruled facial recognition as a technology that violates human rights, personal freedom, invades privacy, and carries discrimination in its practice but not to eliminate it, just utilize it for greater goods (Fernandez, 2020).

AI has a larger barrier of discriminatory issuing that flaws the technology. "In fact, Asians and African Americans are 100 times more likely to be misidentified than white men, with Native Americans having the highest rates of false identifications " (Fernandez, 2020). The data training for the technology is set to search for certain racial and color features. For example, if the technology is trained on data that is based on white males, then it will focus on white males. South Wales Police force had used facial recognition to find a known criminal at the Champions League Final which then resulted in 92% of the matches being a false positive. Similarly, Detroit police ran into the same issue of 96% false positives in a search that arrested the wrong man (Fernandez, 2020). For this technology to be used it must be perfected and structured to protect human rights as it has been a flawed system in previous cases.

Conclusion

Artificial Intelligence Facial Recognition technology has become one of the most powerful tools to enhance human rights at the same time it has been orchestrated as weaponry against humanity. This technology stands on a considerable spectrum with two polar opposite conditions. Responsibility to exercise the use of this technology is a concern for the State, government, the international community, the private sector, public companies, and education. To ethically use this technology, transparency must be communicated, and intentions need to be founded on the enhancement of human life.

List of References

Ai Facial Recognition Technology Overview 2021. RecFaces. (2021, August 23). Retrieved November 8, 2021, from https://recfaces.com/articles/ai-facial-recognition

Amnesty International. (2021, August 17). Ban facial recognition technology. Amnesty International. Retrieved November 9, 2021, from https://www.amnesty.org/en/latest/press-release/2021/01/ban-dangerous-facial-recognition-technology-that-amplifies-racist-policing/

Fernandez, E. (2020, August 12). Facial recognition violates human rights, court rules. Forbes. Retrieved November 9, 2021, from https://www.forbes.com/sites/fernandezelizabeth/2020/08/13/facial-recognition-violates-human-rights-court-rules/?sh=44d625675d44

Interesting Engineering. (2020). How does facial recognition work? YouTube. YouTube. Retrieved November 10, 2021, from https://www.youtube.com/watch?v=YX8BzK_LU0E.

Knight, W. (2020, April 21). Mit cuts ties with a Chinese AI firm Amid Human Rights Concerns. Wired. Retrieved November 9, 2021, from https://www.wired.com/story/mit-cuts-ties-chinese-ai-firm-human-rights/

Lu, C. (2021, March 16). Painting Xinjiang's brutal camps in 3D. Foreign Policy. Retrieved December 22, 2021, from https://foreignpolicy.com/2021/03/16/xinjiang-reeducated-documentary-uyghur-china-vr/.

Mozur, P. (2018, July 8). Inside China's dystopian dreams: A.I., shame and lots of cameras. The New York Times. Retrieved November 8, 2021, from https://www.nytimes.com/2018/07/08/business/china-surveillance-technology.html

Neuman, S. (2021, September 16). The U.N. warns that AI can pose a threat to human rights. NPR. Retrieved November 9, 2021, from https://www.npr.org/2021/09/16/1037902314/the-u-n-warns-that-ai-can-pose-a-threat-to-human-rights

ROBERTS, S. E. A. N. R. (2022). War on the Uyghurs: China's internal campaign against a Muslim minority. PRINCETON UNIV PRESS

United Nations. (2021, September 15). Urgent action needed over artificial intelligence risks to human rights | | UN news. United Nations: UN News Global perspective Human stories. Retrieved November 9, 2021, from https://news.un.org/en/story/2021/09/1099972

YouTube. (2018). China: facial recognition and state control | The Economist. YouTube. Retrieved November 10, 2021, from https://www.youtube.com/watch?v=lH2gMNrUuEY&t=273s.

Author Short Bio

PLEASE NOTE THAT ALL OPINIONS EXPRESSED IN THIS ARTICLE ARE THOSE OF THE AUTHOR ONLY AND DO NOT REPRESENT THE OFFICIAL POSITION OF THE UNIVERSITY FOR PEACE

# data[].attributes.attachment_link.title
Human Rights in AI Facial Recognition - Ideas for Peace

# data[].attributes.attachment_link.description
AI Facial Recognition has become one of the most controversial topics in the digital era. It is a successful tool and measure in biometrical technological advances. However, it has ethical barriers and dangers to human rights, especially the right to privacy.

# data[].attributes.body
China has been discriminating against its Uyghur minority population for a decade, subjecting Chinese Turkic Muslims to mass detention camps, torture, and execution. China uses facial recognition to racially profile Uyghurs for arrest. In a single month, the city of Sanmenxia has even run this Uyghur-identifying algorithm 500,000 times on its residents.

# data[].attributes.body
Homeland Security has recently begun using facial recognition to restart cold cases, such as child exploitation cases. With the use of Clearview AI, Homeland Security has solved years-old cases that have brought hundreds of previously un-caught pedophiles to justice. From the Kantian perspective, this reward is positive because the intentions are very much in the right.

# data[].attributes.body
China has one of the lowest crime rates and enjoys some of the best security in the world. It is doubtless that facial recognition technology has played a large role in this. China is conducting a massive behavioral engineering agenda, which uses facial recognition to humiliate J-walkers, shoplifters, and other people who have committed minor offenses. This creates a sense of fear in the citizens, strongly discouraging any rule-breaking. From a Virtue-Ethics perspective, this is not an issue. This method China is using promotes virtuous people. The question is, how is a virtuous action defined, and by whom?

# data[].attributes.attachment_link.title
China among countries with lowest crime rate as violent crime plummets over past five years: top procuratorate

# data[].attributes.attachment_link.description
The number of people prosecuted for violent crimes such as homicide, robbery and abduction in 2022 was the lowest in nearly two decades, and China now enjoys one of the lowest crime rates and highest sense of security scores globally, China's top procuratorate said on Wednesday, citing data.

# data[].attributes.attachment_link.readable_text
Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking. And while it's still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China.

China's facial recognition system logs nearly every single citizen in the country, with a vast network of cameras across the country. A database leak in 2019 gave a glimpse of how pervasive China's surveillance tools are -- with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old.

The Chinese government is accused of using facial recognition to commit atrocities against Uyghur Muslims, relying on the technology to carry out " the largest mass incarceration of a minority population in the world today."

"China uses facial recognition to profile Uyghur individuals, classify them on the basis of their ethnicity, and single them out for tracking, mistreatment, and detention," a bipartisan group of 17 senators said in a letter to Secretary of State Mike Pompeo on March 11. "And these technologies are deployed in service of a dystopian vision for technology governance, that harnesses the economic benefits of the internet in the absence of political freedom and sees technology companies as instruments of state power."

China's aggressive development and use of facial recognition offers a window into how a technology that can be both benign and beneficial -- think your iPhone's Face ID -- can also be twisted to enable a crackdown on actions that the average person may not even consider a crime. Chinese officials have used surveillance tools to publicly shame people wearing sleepwear in public, calling it "uncivilized behavior."

The punishing of these minor offenses is by design, surveillance experts said. The threat of public humiliation through facial recognition helps Chinese officials direct over a billion people toward what it considers acceptable behavior, from what you wear to how you cross the street.

"The idea is that the authorities are trying to put in place comprehensive surveillance and behavioral engineering on a mass scale," said Maya Wang, a senior researcher on China at the Human Rights Watch. "The authorities want to create a kind of society that would be very easy for them to manage."

Behavioral engineering

The idea that China's facial recognition would automatically put your name and photo on a billboard for jaywalking and privately text you a fine instills fear in people to behave a certain way, Wang said.

Technology has often had that ability to affect behavior, even outside of China.

Behavioral engineering is the concept of combining technology and psychology to nudge people to act a certain way, and it's something we see every day. It's evident in " dark patterns," interface designs like hidden opt-out buttons that trick people into giving up their personal data, for example.

But there's a key difference in how behavioral engineering is carried out in the US compared with China and its facial recognition.

"It's expressed in a commercial manner in the US, whereas in China it's a state effort," Wang said. "The dynamics are different, and the amount of power is different, but it has striking similarities."

In the US, behavioral engineering can be done through amassing data on people, and pushing or excluding content to them based on predicted personality traits.

Facebook's 2018 scandal with Cambridge Analytica stemmed from the fact that the now-defunct UK data analytics firm used data from millions of people to target advertising that would sway people to vote a certain way.

Russia's 2016 US election meddling efforts involved using Facebook to create divisive groups and stage events where people would show up to protest. These posts would be targeted to police groups or specifically to Spanish speakers, for example.

While that type of behavioral engineering is mostly geared for selling products and profits, China's push is more about instilling fear and control over its population -- and facial recognition plays a key part in that.

"It conditions the idea of 'this is how society works,' that you're always being watched," said Jake Laperruque, a senior counsel at the Constitution Project. "It seems like trying to reinforce to people that 'we're watching all the time, if you ever do anything to piss us off, we will see and we will find a way to embarrass you.'"

China's track record

In China, no one is safe from facial recognition, or the public shaming that comes with it.

Cameras set up at crosswalks to identify and post photos of jaywalkers are commonplace, and a report from Abacus in May 2019 showed photos of children jaywalking on a digital billboard. The local traffic police said "children should be treated the same as adults," according to the outlet.

A Chinese park has also relied on facial recognition to prevent people from taking too much toilet paper, scanning people's faces before dispensing out the rolls.

"It's kind of an 'anything goes' system there, no matter how minor," the Constitution Project's Laperruque said. "Any sort of offense goes for this kind of tech."

And while in the US, facial recognition is going through a reckoning over its racial bias and human rights concerns, in China, the surveillance technology's providers boast its abilities to single out people of different ethnicities.

"There's no allowed space at all, both online and offline, to discuss racial bias and the persecution of minorities in China."

Maya Wang, senior researcher on China at the Human Rights Watch

Last November, IPVM found that Chinese surveillance company Hikvision marketed that its cameras could automatically identify Uyghur Muslims with its facial recognition.

There's pushback on facial recognition in the US because researchers are able to bring to light their concerns about the technology's racial bias, but there isn't similar scrutiny in China, researchers said.

China's facial recognition accuracy rates aren't challenged, even when it accidentally picks up a person on a bus ad and considers them a jaywalker.

"Americans can talk about racial inequalities without fear," the Human Rights Watch's Wang said. "These systems are not discussed or pushed back in China. There's no allowed space at all, both online and offline, to discuss racial bias and the persecution of minorities in China."

Recognizing influence

China's model of facial recognition, from its oppressive surveillance of Uyghur Muslims to its every day grip over a population of 1.4 billion people, also poses a concern for the international community, US lawmakers said.

Last September, the Carnegie Endowment for International Peace found that China was a major supplier of AI surveillance, providing the technology to 63 countries.The next closest provider for AI surveillance tech was Japan's NEC Corporation, which sells facial recognition to 14 countries.

In April, Reuters reported that Amazon bought cameras from Dahua, a Chinese surveillance company blacklisted by the US over allegations that it helps China detain and monitor Uyghur Muslims.

The $10 million deal was for 1,500 cameras to help monitor the spread of COVID-19, according to the report.

In the senators' letter on March 11, they raised issues about China's influence on how facial recognition should be used when it's the technology's top exporter.

Wang noted that while facial recognition is often a subsidiary for US tech giants, like Amazon's Rekognition or Microsoft, in China, there are multiple companies already dominating the industry. The country's acceptance of a surveillance state allows facial recognition providers to push forward with the technology, even for the most trivial uses.

With that level of influence, there're concerns that China's model of how to use facial recognition -- a widespread network designed for public shaming and control -- could spread to the rest of the world.

"Unfortunately, China has indicated a willingness to use standard setting bodies in perverse ways to normalize global opinions about Orwellian surveillance technology," the group of lawmakers said. "By shaping the debate about the legitimate uses of artificial intelligence and facial recognition, China can expand opportunities for countries, particularly those in the developing world, to utilize Chinese surveillance technology."

# data[].attributes.attachment_link.title
How China uses facial recognition to control human behavior

# data[].attributes.attachment_link.description
Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking.

# data[].attributes.body
Governments could pass laws and policies that require the FBI and Homeland Security to be transparent about their use of facial recognition, as well as the software itself. This will increase public awareness, and should an issue arise, the public can push for change.

# data[].attributes.body
Legislature could be passed to ensure that every person with access to facial recognition technology is trained to mitigate bias. In addition, the training data used to create the facial recognition models could be ensured to be diverse, and inclusive of all races. This would dramatically reduce the number of harmful false positives and negatives.

# data[].attributes.body
Currently, the government scrapes up billions of faces from the internet to identify criminals un-consensually. This issue could be resolved by just asking for consent for criminal investigation use when a photo is posted to the internet. Another, less controversial way to attain consent for use could be to just have a subtle warning when someone posts a photo on the internet that says that the image may be used for criminal investigation should the image be posted.

# data[].attributes.attachment_link.title
Eight Months Pregnant and Arrested After False Facial Recognition Match

# data[].attributes.attachment_link.description
Porcha Woodruff thought the police who showed up at her door to arrest her for carjacking were joking. She is the first woman known to be wrongfully accused as a result of facial recognition technology.