Facebook Facial Recognition Hostility, Not For Google
Aracely Oubre
Since the face is one of the most important cues in social interaction, there has also been accumulating evidence that the hostile attribution bias leads to a characteristic misperception of facial expressions. For example, inmates diagnosed with antisocial personality disorder or psychopathy have been found to show deficits in emotion expression recognition [7,8,9]. While hostile intentions could in theory be ascribed to any ambiguous facial expression, the bias seems to be triggered most strongly when the expression contains some amount of anger [10].
Facebook Facial Recognition hostility, not for Google
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There was also a main effect for face gender, in that the expressions of female faces were easier to recognize, across all participant groups (Table 3). This was especially true for disgust and sadness, as indicated by the face gender by expression interaction, as these were significantly easier to recognize in the female models. Overall, the results indicate that no inmate group showed grossly impaired recognition of full-blown facial expressions.
Amnesty International is also calling for a global ban on the development, sale and use of facial recognition technology for surveillance purposes. The organization has recently documented human rights risks linked to facial recognition technology in India and the US, as part of its Ban the Scan campaign.
In occupied East Jerusalem, Israel operates a network of thousands of CCTV cameras across the Old City, known as Mabat 2000. Since 2017, Israeli authorities have been upgrading this system to enhance its facial recognition capabilities and give themselves unprecedented powers of surveillance.
From the historical surveillance of civil rights leaders by the Federal Bureau of Investigation (FBI) to the current misuse of facial recognition technologies, surveillance patterns often reflect existing societal biases and build upon harmful and virtuous cycles. Facial recognition and other surveillance technologies also enable more precise discrimination, especially as law enforcement agencies continue to make misinformed, predictive decisions around arrest and detainment that disproportionately impact marginalized populations.
In this paper, we present the case for stronger federal privacy protections with proscriptive guardrails for the public and private sectors to mitigate the high risks that are associated with the development and procurement of surveillance technologies. We also discuss the role of federal agencies in addressing the purposes and uses of facial recognition and other monitoring tools under their jurisdiction, as well as increased training for state and local law enforcement agencies to prevent the unfair or inaccurate profiling of people of color. We conclude the paper with a series of proposals that lean either toward clear restrictions on the use of surveillance technologies in certain contexts, or greater accountability and oversight mechanisms, including audits, policy interventions, and more inclusive technical designs.
Although suspicion toward communities of color has historical roots that span decades, new developments like facial recognition technologies (FRT) and machine learning algorithms have drastically enlarged the precision and scope of potential surveillance.14 Federal, state, and local law enforcement agencies often rely upon tools developed within the private sector, and, in certain cases, can access massive amounts of data either stored on private cloud servers or hardware (e.g., smartphones or hard drives) or available in public places like social media or online forums.15 In particular, several government agencies have purchased access to precise geolocation history from data aggregators that compile information from smartphone apps or wearable devices. In the general absence of stronger privacy protections at the federal or state levels to account for such advancements in technology, enhanced forms of surveillance used by police officers pose significant risks to civilians already targeted in the criminal justice system and further the historical biases affecting communities of color. Next, we present tangible examples of how the private and public sectors both play a critical role in amplifying the reach of law enforcement through facial recognition and other surveillance technologies.
Facial recognition has become a commonplace tool for law enforcement officers at both the federal and municipal levels. Out of the approximately 42 federal agencies that employ law enforcement officers, the Government Accountability Office (GAO) discovered in 2021 that about 20, or half, used facial recognition. In 2016, Georgetown Law researchers estimated that approximately one out of four state and local law enforcement agencies had access to the technology.16
But Clearview AI is only one of numerous private companies that U.S. government agencies partner with to collect and process personal information.19 Another example is Vigilant Solutions, which captures image and location information of license plates from billions of cars parked outside homes, stores, and office buildings, and which had sold access to its databases to approximately 3,000 local law enforcement agencies as of 2016.20 Vigilant also markets various facial recognition products like FaceSearch to federal, state, and local law enforcement agencies; its customer base includes the DOJ and DHS, among others.21 A third company, ODIN Intelligence, partners with police departments and local government agencies to maintain a database of individuals experiencing homelessness, using facial recognition to identify them and search for sensitive personal information such as age, arrest history, temporary housing history, and known associates.22
In the end, it is virtually impossible for an individual to fully opt out of facial recognition identification or control the use of their images without abstaining from public areas, the internet, or society altogether.
As both the government and private corporations feed into the problem of surveillance, gaps in current federal and state privacy laws mean that their actions to collect, use, or share data often go unchallenged. In other words, existing laws do not adequately protect user privacy among the rising ubiquity of facial recognition and other emerging technologies, fundamentally omitting the needs of communities of color that disproportionately bear the consequences of surveillance. To reduce the potential for emerging technologies to replicate historical biases in law enforcement, we summarize recent proposals that address racial bias and unequal applications of technology in the public sector. We also explain why U.S. federal privacy legislation is necessary to govern how private sector companies implement fairness in the technical development process, limit their data collection and third-party sharing, and grant more agency to the individuals they surveil.
With Amazon, Google, and Microsoft all developing their own software, it seems likely the use of facial recognition software will increase in the near future. Unlike other identification methods, like a fingerprint scan or an ID card, facial recognition can be used on people without them noticing or, crucially, giving their consent.
Worryingly, inaccurate readings may be more likely to affect some groups of people than others. A recent test on Idemia, a facial recognition program widely used by law enforcement agencies, found that black women may be ten times more likely to receive a false match than white women.
As a sort of worst-case scenario, a think tank out of Berkeley made a presentation in 2017 called Slaughterbots, which hypothesized that, in the near future, it would be entirely possible for a terrorist to create a weaponized drone that could use facial recognition technology to locate and assassinate anyone they wanted.
Then, on September 3, 2019, Facebook announced it would turn off all facial recognition features by default but only for new accounts. The billions of existing Facebook users will still have to manually opt-out of Tag Suggestions and other facial recognition features. For information on how you can do this, check out this article.
Baseline assessment comprised a total of 145 participants including 57 essential hypertensive and 65 normotensive men who were otherwise healthy and medication-free. Seventy-two eligible participants additionally completed follow-up assessment 3.1 (0.08 SEM) years later to analyze BP changes over time. We assessed emotion recognition of facial affect with a paradigm displaying mixed facial affect of two morphed basic emotions including anger, fear, sadness, and happiness. Trait anger was assessed with the Spielberger trait anger scale.
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