Regulating Facial Recognition Technology to Reduce Bias

By Khortlan Becton, JD, MTS, The Restorative Education Institute (1)

This blog post is an excerpt from the 2023 Hooks Institute Policy Papers “The Promise and Peril: Unpacking the Impact of A.I. and Automation on Marginalized Communities.” Read more here.

I. Introduction

From unlocking a phone to identifying shoplifters in real-time, facial recognition technology (“FRT”) use is increasing among private companies and having an increasingly large impact on the public. According to one study, the global FRT market is expected to grow from $3.8 billion in 2020 to $8.5 billion by 2025 (MarketsandMarkets, 2020). Domestically, FRTs are a central aspect of artificial intelligence (“AI”) use and development in the U.S. pri- vate sector. The following statistics demonstrate the growing prevalence of FRT use in the U.S. private sector: 72% of hotel operators are expected to deploy FRTs by 2025 to identify and interact with guests; by 2023, 97% of airports will roll out FRTs; excluding Southwest Airlines, most major US airlines currently use FRTs (Calvello, 2019).

This explosion of private FRT use has prompted many professional organizations and community organizers to call for a moratorium on FRT use until the enactment of state and federal regulatory actions. One such group noted that industry and government have adopted FRTs “ahead of the development of principles and regulations to reliably assure their consistently appropriate and non-prejudicial use” (Association for Computing Machinery [ACM], 2020). Among the stakeholders calling for such moratoriums is a concern over the alarming level of bias present within commercial FRT systems. Given the widespread integration of FRTs throughout society, both presently and to come, the presence of bias in FRTs is particularly troublesome as decision-making driven by biased FRT can lead to significant physical and legal injuries. For example, self-driving cars are more likely to hit dark-skinned pedestrians (Samuel, 2019). Biased FRTs also have the likelihood of producing discriminatory hiring decisions, credit approvals, or mortgage approvals.

Though the observable and conceivable consequences of bias in FRTs are virtually boundless, state and federal regulatory schemes have not adapted to the growth of FRTs. A continuing lag in regulations designed to address bias in FRTs will likely lead to a range of discriminatory effects that existing agencies do not have the capacity to prevent or redress. Therefore, a federal regulatory scheme propagated by a new agency specifically authorized to regulate AI technologies will better ensure the governance of private entities’ use of facial recognition technologies to address bias than the current regulatory scheme.

A. The Relationship Between AI and FRTs

In popular usage, AI refers to the ability of a computer or machine to mimic the capabilities of the human mind and combining these and other capabilities to perform functions a human might perform (IBM, 2020). AI-powered machines are usually classified into two groups—general and narrow (Towards Data Science, 2018). Narrow AI, which drives most of the AI that surrounds us today, is trained and focused to perform specific tasks. (IBM, 2020). General AI is AI that more fully replicates the autonomy of the human brain—AI that can solve many types of problems and even choose the problems it wants to solve without human intervention (IBM, 2020).

Machine learning is a subset of AI application that enables an application to progressively reprogram itself, digesting data input by human users, to perform the specific task the application is designed to perform with increasingly greater accuracy (IBM, 2020). Deep learning, a subset of machine learning, allows applications to automatically identify the features to be used for classification, without human intervention (IBM, 2020).

Facial recognition technologies are artificial intelligence systems programmed to identify or verify the identity of a person using their face (Thales Group, 2021). “A general statement of the problem of machine recognition of faces can be formulated as follows: given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces” (Chellappa et al., 2003). Face recognition is often described as a process that first involves four steps: face detection, face alignment, feature extraction, and face recognition (Brownlee, 2019).

  1. Face Detection. Locate one or more faces in the image with a bounding box.
  2. Face Alignment. Normalize the face to be consistent with the database, such as geometry and
  3. photometrics.
  4. Feature Extraction. Extract features from the face that can be used for the recognition task.
  5. Face Recognition. Perform matching of the face against one or more known faces in a prepared database (Brownlee, 2019).

Companies are developing and implementing FRTs in new and potentially beneficial ways, such as: helping news organizations identify celebrities in their coverage of significant events, providing secondary authentication for mobile applications, automatically indexing image and video files for media and entertainment companies, and allowing humanitarian groups to identify and rescue human trafficking victims (Amazon Web Services [AWS], 2021). Recently, FRT has been in the news for its application in the investigation of the Jan. 6, 2021, Capital riot (Sakin, 2021). Other news stories about facial recognition have centered on the coronavirus pandemic. One business proposed creating immunity passports for those who are no longer at risk of contracting or spreading COVID-19 and to use FRTs to identify the immunity passport holder (Sakin, 2021). A MarketsandMarkets (2020) study estimates that the global facial recognition market is expected to grow from $3.8 billion in 2020 to $8.5 billion by 2025.

The Federal Trade Commission’s (“FTC”) recent settlement with Everalbum, a California-based developer of a photo storage app, exemplifies the growth of FRT use in the commercial sector and the liabilities companies may face for implementing the technology. In its complaint, the FTC alleged that Everalbum, which offered an app that allowed users to upload photos and videos to be stored and organized, launched a new feature that, by default, used face recognition to group users’ photos by faces of the people who appear in the photos (Everalbum, Inc., n.d.). Everalbum also allegedly used, without affirmative express consent, users’ uploaded photos to train and develop its own FRT (Everalbum, Inc., n.d.). Regarding its implementation of FRTs, the FTC charged Everalbum
for engaging in unfair or deceptive acts or practices, in violation of Section 5(a) of the Federal Trade Commission Act, by misrepresenting that it was not using facial recognition unless the user enabled it or turned it on (Everal- bum, Inc., n.d.). In January 2021, Everalbum settled the FTC allegations concerning its deceptive use of FRTs. The proposed settlement requires Everalbum to delete all face embeddings the company derived from photos of users who did not give their express consent to their use and any facial recognition models or algorithms developed with users’ photos or videos (Everalbum, Inc., n.d.). The company must also obtain a user’s express consent before using biometric information it collected from the user to create face embeddings or develop FRTs (Everalbum, Inc., n.d.). Everalbum’s recent settlement with the FTC underscores the nascency of federal governance of FRTs, as the Everalbum settlement is among the first of few federal agency enforcements targeting commercial use of FRTs (Federal Trade Commission [FTC], 2019) (2). Signaling the potential for increasing regulation and enforcement in this area, FTC Commissioner Rohit Chopra noted that FRT “is fundamentally flawed and reinforces harmful biases” while highlighting the importance of “efforts to enact moratoria or otherwise severely restrict its use” (Federal Trade Commission [FTC], (2021).

B. Bias in Facial Recognition Technologies

Although proponents of FRTs boast high accuracy rates, a growing body of research exposes divergent error rates in FRT use across demographic groups (Najibi, 2020). In the landmark 2018 “Gender Shades” report, MIT and Microsoft researchers applied an intersectional approach to test three commercial gender classification algorithms (Buolamwini & Gebru, 2018). The researchers provided skin type annotations for unique subjects in two datasets and built a new facial image dataset that is balanced by gender and skin type (Buolamwini & Gebru, 2018). Analysis of the dataset benchmarks revealed that all three algorithms performed the worst on darker-skinned females, with error rates up to 34.7% higher than for lighter-skinned males (Buolamwini & Gebru, 2018). The classifiers also performed more effectively on male faces (Buolamwini & Gebru, 2018). The researchers suggested that darker skin may not be the only factor responsible for misclassification and that darker skin may instead be highly correlated with facial geometrics or gender presentation standards (Buolamwini & Gebru, 2018). Noting that default camera settings are often optimized to better expose lighter skin than darker skin, the researchers concluded that under-and overexposed images lose crucial information making them inaccurate measures of classification within artificial intelligence systems (Buolamwini & Gebru, 2018). The report also emphasizes the need for increased diversity of phenotypic and demographic representation in face datasets and algorithmic evaluations since “[i]nclusive benchmark datasets and subgroup accuracy reports will be necessary to increase transparency and accountability in artificial intelligence” (Buolamwini & Gebru, 2018).

In 2019, the National Institute of Standards and Technology (“NIST”) released a series of reports on ongoing face recognition vendor tests (“FRVT”). Using both one-to-one verification algorithms and one-to-many identification search algorithms submitted to the FRVT by corporate research and development laboratories and a few universities, the NIST Information Technology Laboratory quantified the accuracy of face recognition algorithms for demographic groups defined by sex, age, and race or country of origin (Natl. Inst. of Stand. & Technol. [NIST], 2018). The NIST used these algorithms with four large datasets of photographs collected in U.S. governmental applications (3) (Natl. Inst. of Stand. & Technol. [NIST], 2018), which allowed researchers to process a total of 18.27 million images of 8.49 million people through 189 mostly commercial algorithms from 99 developers (Natl. Inst. of Stand. & Technol. [NIST], 2018).

The FRVT report confirms that a majority of the face recognition algorithms tested exhibited demographic differentials of various magnitudes in both false negative results (rejecting a correct match) and false positive results (matching to the wrong person) (Crumpler, 2020). In regard to false positives, the NIST found: (1) that false positive rates are highest in West and East African and East Asian people, and lowest in Eastern European individuals (Natl. Inst. of Stand. & Technol. [NIST], 2018) (4), (2) that, with respect to a number of algorithms developed in China, this effect is reversed, with low false positives rates on East Asian faces; (3) that, with respect to domestic law enforcement images, the highest false positive rates are in American Indians, with elevated rates in African American and Asian populations; (4) and that false positives are higher in women than men, and this is consistent across algorithms and datasets (Natl. Inst. of Stand. & Technol. [NIST], 2018). In regard to false negatives, the NIST found: (1) that false negatives are higher in Asian and American Indian people in domestic mugshots; (2) that false negatives are generally higher in people born in Africa and the Caribbean, the effect being stronger in older individuals (5) (Natl. Inst. of Stand. & Technol. [NIST], 2018).

Encouragingly, the NIST concluded that the differences between demographic groups were far lower in algorithms that were more accurate overall (Natl. Inst. of Stand. & Technol. [NIST], 2018). This conclusion signals that as FRTs continue to evolve, the effects of bias can be reduced (Crumpler, 2020). Based on its finding that the algorithms developed in the U.S. performed worse on East Asian faces than did those developed in China, the NIST theorized that the Chinese teams likely used training datasets with greater representation of Asian faces, improving their performance on that group (Natl. Inst. of Stand. & Technol. [NIST], 2018). Thus, the selection of training data used to build algorithmic models appears to be the most important factor in reducing bias (Crumpler, 2020).

Although both the “Gender Shades” and FRVT reports identify under-representative training sets as major sources of algorithmic bias, another recent study of commercial facial algorithms led by Mei Wang showed that “[a]ll algorithms . . . perform the best on Caucasian testing subsets, followed by Indians from Asia, and the worst on Asians and Africans. This is because the learned representations predominately trained on Caucasians will discard useful information for discerning non-Caucasian faces” (Wang, 2019). Furthermore, “[e]ven with balanced training, we see that non-Caucasians still perform more poorly than Caucasians. The reason may be that faces of coloured skins are more difficult to extract and pre-process feature information, especially in dark situations” (Wang, 2019).

Between 2014 and 2018, the accuracy of facial recognition technology has increased 20-fold (Natl. Inst. of Stand. & Technol. [NIST], 2018). However, further applications of FRT will almost certainly bring new challenges if the prevalence of bias remains unchecked. According to Jan Lunter, co-founder and CEO of Innovatrics, facial recognition companies can approach the issue of bias using the insights that the biometrics industry has gained over the past two decades. “Any failure to use these techniques,” Lunter warns, “will not only fan public mistrust, but also inhibit the iterative pace of improvement shown over the past five years” (Natl. Inst. of Stand. & Technol. [NIST], 2018).

II. Current State and Federal Regulatory Schemes

Against a backdrop of scant federal regulation of commercial AI use, including FRTs, several states have adopted their own regulatory schemes to govern the emergent technology. Illinois (740 Ill Comp. Stat), Washington (Wash. Rev. Code), California (Cal. Civ. Code), and Texas (11 Tex. Bus. & Com. Code) have each enacted legislation that targets private sector use of biometric information, including facial images. The states’ legislative schemes commonly define biometric identifiers that encompass facial images by describing them as “face geometry” or unique biological patterns that identify a person (Yeung et al, 2020). However, the states each employ vastly different methods of enforcement. In Texas and Washington, only the state attorney general has enforcement power (11 Tex. Bus. & Com. Code). In California, the state attorney general and the consumer share responsibility for taking action against entities that violate privacy protections (Cal. Civ. Code). While, in Illinois, any person has the right to pursue action against firms and obtain damages between $1,000 and $5,000 per violation (740 Ill. Comp. Stat). Consequently, companies such as Google, Shutterfly, and Facebook have been sued in Illinois for collecting and tagging consumers’ facial information (Yeung et al., 2020).

Facial recognition bans, which range in scope, are on the rise at the municipal level. In September 2020, Portland, Oregon, banned facial recognition use by both public and private entities, including in places of “public accommodation,” such as restaurants, retail stores and public gathering spaces (Metz, 2020). The Portland, Oregon ban does allow private entities’ use of FRTs (1) to the extent necessary to comply with federal, state, or local laws; (2) for user verification purposes to access the user’s own personal or employer-used communication and electronic devices; or (3) in automatic face detection services in social media apps (Hunton Andrews Kurth LLP, 2020). Similarly, Portland, Maine passed an ordinance in November 2020 banning both the city and its departments and officials from “using or authorizing the use of any facial surveillance software on any groups or members of the public” (Heater, 2020). The ordinance allows members of the public to sue if “facial surveillance data is illegally gathered and/or used” (Heater, 2020). Importantly, the Portland, Maine ban does not apply to private companies.

The federal government’s national AI strategy continues to take shape with constant new developments. On November 17, 2020, the Director of the Office of Management and Budget (“OMB”), pursuant to Executive Order 13859, issued a memorandum addressed to the heads of executive departments and agencies that provided guidance for the regulation of non-governmental applications of “narrow” or “weak” AI (6) (The White House, 2020). The OMB’s memo briefly recognized the potential issues of bias and discrimination in AI applications and recommended that agencies “consider in a transparent manner the impacts that AI applications may have on discrimination.” Specifically, the OMB recommended that when considering regulatory or non-regulatory approaches related to AI applications, “agencies should consider, in accordance with law, issues of fairness and non-discrimination with respect to outcomes and decision produced by the AI application at issue, as well as whether the AI application at issue may reduce levels of unlawful, unfair, or otherwise unintended discrimination as compared to existing processes.”

Pursuant to the National AI Initiative Act of 2020 (The White House, 2020), the Director of the Office of Science and Technology Policy (“OSTP”) formally established the National AI Initiative Office (the “Office”) on January 12, 2021. The Office is responsible for overseeing and implementing a national AI strategy and acting as a central hub for coordination and collaboration for federal agencies and outside stakeholders across government, industry and academia in AI research and policymaking (The White House, 2020). On October 4, 2022, the OSTP released the Blueprint for an AI Bill of Rights (the “Blueprint”), which “identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence” (The White House Office of Science and Tech. Policy [OSTP], 2022a).

The five guiding principles are: 1. Safe and Effective Systems; 2. Algorithmic Discrimination Protections; 3. Data Privacy; 4. Notice and Explanation; and 5. Human Alternatives, Consideration, and Fallback.” (The White House Office of Science and Tech. Policy [OSTP], 2022a). The AI Bill of Rights further provides recommendations for designers, developers, and deployers of automated systems to put these guiding principles into practice for more equitable systems. The Biden-Harris administration has also announced progress across the Federal government that has advanced the Blueprint’s guiding principles, including actions from the Department of Labor, the Equal Employment Opportunity Commission, the Consumer Financial Protection Bureau, and the Federal Trade Commission (“FTC”) (The White House Office of Science and Tech. Policy [OSTP], 2022b).

Most recently, U.S. Senate Majority Leader Charles Schumer has spearheaded efforts to manage AI by circulating a framework that outlines a proposed regulatory regime for AI technologies. Schumer declared on the Senate floor, “Congress must move quickly. Many AI experts have pointed out that the government must have a role in how this technology enters our lives. Even leaders of the industry say they welcome regulation.” Schumer’s nod towards industry leaders is likely in reference to the several congressional panels that held hearings on AI with industry experts during the week of May 16, 2023. Most notably, Sam Altman, the CEO of OpenAI, the company known for promulgating ChatGPT, testified before a Senate committee on May 16, 2023, imploring legislators to regulate the fast-growing AI industry. Altman proposed a three-point plan for regulation that called for: 1. A new government agency with AI licensing authority, 2. The creation of safety standards and evaluations, and 3. Required independent audits. In response to Altman’s plea, Senator Schumer met with a group of bipartisan legislators to begin drafting comprehensive legislation for AI regulation.

The FTC has already taken an active role in regulating private sector development and use of FRT, as evidenced by its recent settlements with Facebook and Everalbum. Further solidifying the FTC’s regulatory stance, acting FTC Chairwoman Rebecca Kelly Slaughter made remarks at the Future of Privacy Forum specifically tying the FTC’s role in addressing systemic racism to the digital divide, AI and algorithmic decision-making, and FRTs (Federal Trade Commission [FTC], 2019).

On April 19, 2021, the FTC published a blog post announcing the Commission’s intent to bring enforcement actions related to “biased algorithms” under section 5 of the FTC Act, the Fair Credit Reporting Act, and the Equal Credit Opportunity Act (Federal Trade Commission [FTC], 2021). Importantly, the statement expressly notes that, “the sale or use of—for example—racially biased algorithms” falls within the scope of the FTC’s prohibition of unfair or deceptive business practices (Federal Trade Commission [FTC], 2021). The FTC also provides guidance on how companies can “do more good than harm” in developing and using AI algorithms by auditing its training data and, if necessary, “limit[ing] where or how [they] use the model;” testing its algorithms for improper bias before and during deployment; employing transparency frameworks and independent standards; and being transparent with consumers and seeking appropriate consent to use consumer data (Federal Trade Commission [FTC], 2021).

III. Argument

The fledgling federal, state, and municipal AI and FRT regulations exist in a loose patchwork that will likely complicate enforcement and compliance for private companies. These complications could hamper or, in some cases, de-incentivize the reduction of bias in FRTs as companies could seek shelter in whichever jurisdiction is most permissive. The federal government’s creation of the National AI Initiative Office does not ensure reductions in FRT bias because the Office is primarily authorized to facilitate AI innovation and cooperation between the government and private companies, rather than addressing any inherent biases present in the FRTs. The FTC’s recent enforcements against private use of FRTs and its recent guidelines indicate that it has an interest in addressing the use of FRTs and FRT bias. However, the FTC possesses limited authority in this context and has historically struggled to compel compliance from large corporations. Thus, a new agency, specifically authorized to regulate and eliminate issues of bias that arise from commercial FRT applications, is needed to effectively address the presence and effect of bias within FRTs.

A. The States’ privacy protections for consumers, comprised of a patchwork of state and municipal regula tions, are inadequate to sufficiently address the issues of bias anticipated from the commercial use of FRTs.

In the absence of federal laws that regulate the commercial use of AI, much less FRTs, state and city laws have attempted to fill the regulatory gap. State governments may be regarded and valued as “living laboratories” in some respects, but their collective piecemeal legislation concerning commercial AI and FRT use may negatively impact the reduction of bias in FRTs and could likely lead to a deregulatory “race to the bottom.”

Significantly, three states–Illinois, Texas and Washington—have recognized the urgent need to address the burgeoning use of AI in the private sector and put privacy protections in place for consumers. The Illinois Biometric Information Privacy Act, passed in 2008, requires commercial entities to obtain written consent in order to capture an individual’s biometric identifiers (including face geometry) or sell or disclose a person’s biometric identifier (740 Ill. Comp. Stat). The Illinois Act also places security and retention requirements on any collected biometric data (740 Ill. Comp. Stat). Although Texas and Washington have enacted similar laws, their laws vary significantly from Illinois’ in that only the attorney generals are authorized to enforce the laws against commercial entities (11 Tex. Bus. & Com. Code). Illinois’ law, on the other hand, includes a private right of action, which has led to several lawsuits against companies such as Clearview AI, Google, and Facebook (Greenberg, 2020; Yeung et al, 2020).

The variance among the entities empowered to enforce these states’ laws will likely create enforcement and compliance difficulties, particularly as it pertains to bias, because AI and FRTs inherently transcend state borders. Based on recent studies of the presence of bias in commercial FRTs, the selection of training data used to build algorithmic models appears to be the most important factor in reducing bias (Crumpler, 2020). Thus, the reduction of bias in commercial FRTs would be significantly hindered if companies are unsure whether they have access to certain images based on specific state laws. For example, Everalbum’s settlement with the FTC revealed that the international company compiled FRT training datasets by combining facial images it had extracted from Ever users’ photos with facial images obtained from publicly available datasets (Everalbum, Inc., n.d.). Everalbum’s FRT development was geographically constrained on a state-by-state basis to exclude images from users believed to be residents of Illinois, Texas, Washington, or the European Union (Everalbum, Inc., n.d.). From the perspective of increasing representative training datasets, the company’s exclusion of facial images from users in Texas and Illinois, specifically, would have negatively impacted the representation of Latinx people and other racial minorities (7) (Krogstad, 2020).

Given uncertainty among AI and FRT developers within the patchwork state regulatory scheme, paired with researchers’ recommendations to increase phenotypic and demographic representation in face datasets and algorithmic evaluations (Buolamwini & Gebru, 2018), companies will likely want to conduct business in locations that enable them to have access to large amounts of data. In response, states may avoid enacting AI and FRT regulations that deter companies from conducting business in those states, resulting in what is termed as a deregulatory “race to the bottom” (Chen, 2022). If a “race to the bottom” situation was to occur in response to the patchwork of state AI regulations, then companies would likely seek to build and train FRTs in those states where consumers had less rights to their biometric data since the companies would have access to more information to compile larger datasets.

On the one hand, enabling companies’ ability to compile larger datasets seems like a great avenue to reduce bias in FRT applications, as the larger datasets would provide increased phenotypic and demographic diversity. However, a lack of state standards governing the quality and collection of biometric data could negatively impact FRT accuracy and, in turn, exacerbate the presence of biased results. According to one study, non-Caucasians may perform more poorly than Caucasians on FRTs, even with balanced training, because “faces of coloured skins are more difficult to extract and pre-process feature information, especially in dark situations” (Wang et al., 2019). Similarly, the “Gender Shade” researchers noted that default camera settings are often optimized to better expose lighter skin than darker skin (Buolamwini & Gebru, 2018). This observation led the researchers to conclude that under-and overexposed images lose crucial information making them inaccurate measures of classification within artificial intelligence systems (Buolamwini & Gebru, 2018). If biased FRT performance is linked to the difficulty of extracting and pre-processing feature information from non-Caucasian faces, especially in dark situations; and, if sub-optimal camera lightening of non-Caucasian faces often produces images that lack crucial information rendering them inaccurate datapoints; then, lax state regulations on the quality and collection of biometric data will likely widen the discrepancy between FRTs’ performance on Caucasian and non-Caucasian faces, undermining efforts to reduce bias in commercial FRT use.

B. The current federal regulatory scheme lacks the scope and capacity to sufficiently address the issues of bias anticipated from the commercial use of FRTs.

The U.S. federal government, in passing the National AI Initiative Act of 2020 and creating the National AI Initiative Office (the “Office”), decided to primarily focus its resources on the support and growth of AI and its attendant technologies, including FRTs (Gibson, Dunn & Crutcher LLP, 2021). The Act also (1) expanded and made permanent the Select Committee on AI, which will serve as the senior interagency body responsible for overseeing the National AI Initiative; (2) codified the National AI Research Institutes and the National Sciences Foundation, collaborative institutes that will focus on a range of AI research and development areas, into law; (3) expanded AI technical standards to include an AI risk assessment framework; and (4) codified an annual AI budget rollup of Federal AI research and development investments (The White House Office of Science and Tech. Policy [OSTP], 2021). Further, on January 27, 2021, President Biden signed a memorandum titled, “Restoring trust in government through science and integrity and evidence-based policy making,” setting in motion a broad review of federal scientific integrity policies and directing agencies to bolster their efforts to support evidence-based decisions making (The White House Office of Science and Tech. Policy [OSTP], 2021). In spite of these nascent attempts to federally regulate commercial use of FRTs, the existing commercial applications of FRTs and the instances of bias that arise from such use remain largely unregulated.

The National AI Initiative Office lacks the capacity and authority to regulate bias arising from current commercial FRT use since, according to its enabling statute, the Office is principally concerned with supporting public and private AI innovation. The National AI Initiative Act describes the Office’s responsibilities as serving as a liaison between the government, industry, and academia; outreaching to the public, and promoting innovation (The White House, 2020).

None of the enumerated responsibilities described in the National AI Initiative Act authorize the Office to specifically regulate existing commercial AI use, let alone address any issues of bias. The first two responsibilities establish the Office’s authority to “provide technical and administrative support” to other federal AI Initiative committees and serve as a liaison on federal AI activities between a broadly defined group of public and private entities. The last two responsibilities charge the Office with reaching out to “diverse stakeholders” and promoting interagency access to the AI Initiative’s activities. The Office’s enabling statute does not clearly indicate whether the regulatory body has enforcement authority on private actors as there is no provision that confers on the Office the ability to promulgate rules or regulations. Likewise, the Office does not seem to have the power to impose sanctions in order to ensure industry compliance. Instead, the Office is focused on building coordination between the private sector and governmental entities to promote further AI innovation. Thus, the Office does not have explicit regulatory authority over any existing private use of AI or FRTs.

Supporters of the National AI Initiative Act and the Office may argue that the Office is appropriately situated to address issues of bias arising from the commercial use of FRTs, however that argument is undermined by the express statutory language of the Act. A supporter of the Office may point to the entity’s responsibility to serve as a liaison on federal AI activities between public and private entities to argue that, by facilitating the exchange of technical and programmatic information that could address bias in AI, the Office would help FRT developers reduce bias. However, the statute does not appear to enable the Office to influence or contribute to the substantive contents of the information shared between the public and private sectors about the AI Initiative activities. If the Office lacks the ability to influence the substance of information exchanged, then it also lacks the ability to specifically direct information sharing that could redress bias in commercial AI applications. A supporter of the Office may also point to its outreach responsibility to argue that the Office will work to address bias by reaching out to diverse stakeholders, including civil rights and disability rights organizations. Yet, the statutory language is vague as to the substance of this “regular public outreach” responsibility. Without a clearer indication that the Office’s public outreach efforts are directed toward or will somehow result in a reduction in AI and FRT bias, the assumption that coordinating public outreach with diverse stakeholders will sufficiently address bias in commercial FRT use remains unfounded. Hence, reducing bias that arises from the commercial use of FRTs is not an articulated central focus, nor an explicitly intended effect, of the Office’s enabling statute.

Close analysis of the statutory language establishing the National AI Initiative and the Office reveals that the Office will likely operate more like a governmental think-tank to ensure coordinated AI innovation than a regulatory body with enforcement power. Such a scheme is inadequate to properly address the existing issues of bias shown in today’s commercial FRTs since the AI Initiative will likely promulgate industry standards that stem from and reflect the market itself, including its apparent biases.

The few FTC regulatory decisions that have been handed down concerning existing commercial FRT applications are products of the FTC’s recent actions to regulate private AI use (Facebook, Inc., n.d.). Based on its latest posts and statements, the FTC anticipates broadening its regulation of private AI and FRT use to not only focus on user consent, but also biased algorithms (Jillson, 2021). However, the FTC has limited enforcement power to sufficiently address the wide-ranging applications of FRTs and reduce the perpetuation of bias.

The Federal Trade Commission Act empowers the FTC to, among other things:

(a) prevent unfair methods of competition and unfair or deceptive acts or practices in or affecting commerce;
(b) seek monetary redress and other relief for conduct injurious to consumers; and
(c) prescribe rules defining with specificity acts or practices that are unfair or deceptive, and establishing requirements designed to prevent such acts or practices (15 U.S.C. §§ 41-58).

As stated in its enabling statute, the FTC’s enforcement power is limited to “unfair or deceptive acts or practices in or affecting commerce” (15 U.S.C. §§ 41-58) The FTC asserts its authority over certain issues or subject areas by deeming a certain commercial practice unfair or deceptive, which is exactly what the FTC did when it released its recent AI blog post categorizing the use or sell of “biased algorithms” as an unfair and deceptive practice. Yet, the FTC will likely run into future enforcement issues in trying to prevent the use and sale of biased algorithms because they lack the willingness to enforce orders and expertise in AI training and development. Despite the FTC’s recent blog post indicating its intention to bring enforcement actions related to biased algorithms, FTC Commissioner Rohit Chopra provided a statement to the Senate noting that “Congress and the Commission must implement major changes when it comes to stopping repeat offenders” and that “since the Commission has shown it often lacks the will to enforce agency orders, Congress should allow victims and state attorneys general to seek injunctive relief in court to halt violations of FTC orders (Federal Trade Commission [FTC], 2021).

In support of his first suggestion concerning the issue of repeat offenders, Commissioner Chopra emphasized that, “[w]hile the FTC is quick to bring down the hammer on small businesses, companies like Google know that the FTC simply is not serious about holding them accountable” (Federal Trade Commission [FTC], 2021). If the FTC is currently struggling to “turn the page on [their] perceived powerlessness” (Federal Trade Commission [FTC], 2021), then it follows that it is most likely ill-suited to successfully take on emerging global leaders in commercial AI technology. Furthermore, the Commissioner’s plea for Congress to allow victims and state attorneys general to access the courts for injunctive relief underscores the FTC’s inability and unwillingness to enforce its orders. Shifting the burden onto consumers and judges to regulate the exploding commercial use of FRTs and reduce bias is less than ideal as the courts lack the expertise and resources to adequately address bias in commercial FRT use. Also, courts are bound by justiciability principles, which limits their ability to regulate and reduce bias. Therefore, Congress should create a new agency that is solely authorized to address issues of bias in commercial FRT use, has power to regulate, and teeth to go after private parties who violate its regulations.

C. Congress must establish a new federal agency specifically, but not solely, authorized to regulate and eliminate issues of bias that arise from commercial FRT applications.

In order to effectively address the pervasiveness of bias in private FRT use, Congress must establish a new regulatory agency specifically, but not solely, authorized to regulate and eliminate issues of bias that arise from commercial FRT applications. The new agency should be created according to the following enabling statute to ensure its appropriate scope and capacity:

The [agency] is empowered, among other things, to:

(a) prevent private entities’ development, use, or sale of FRTs in circumstances that perpetuate bias based on ethnic, racial, gender, and other human characteristics recognizable by computer systems;
(b) seek monetary redress or other relief for injuries resulting from the presence of bias in FRTs; (c) prescribe rules and regulations defining with specificity circumstances known or reasonably foreseeable to perpetuate bias that is prejudicial to established human and legal rights, and establishing standards designed to prevent such circumstances;
(d) gather and compile data and conduct investigations related to private entities’ development, testing, and application of FRTs; and
(e) make reports and legislative recommendations to Congress and the public. (8)

Part (a) of the new agency’s enabling statute delineates the scope of the agency’s enforcement power to specifically regulate private entities’ development, use or sale of FRTs in settings that perpetuate bias. The phrase “development, use, or sale” is designed to extend the agency’s regulatory scope to include the development or creation of FRTs in recognition of the fact that biases can originate from either the algorithm or the training dataset. Including the development stage within the agency’s regulatory authority will allow the agency to effectively regulate the sources of bias—the algorithm, training datasets, and photo quality. Additionally, the inclusion of all three stages—development, use, and sale—enable the agency to have the conceptual framework and authority to regulate any future sources of bias that are yet to be discovered (9) (Learned-Miller et al., 2020).

Part (b) confers the agency the power to impose sanctions in the form of monetary penalties or other appropriate type of relief for injuries caused by a private party’s violation of the agency’s regulations. Part (b) is of utmost importance since it will give the agency power to bring down the hammer on violating entities and shirk the perception of “powerlessness.” The agency will compel compliance from large companies by bringing timely actions against violating parties, requiring violating parties to make material changes to their algorithms that eliminate or significantly reduce bias, and maintaining a reputation for rigorously holding companies accountable for their algorithms.

Part (c) functions hand-in-hand with Part (b) in that the agency’s promulgation of rules and regulations creates the legal claims through which the agency can seek monetary redress or other forms of relief from violating companies. Requiring the agency to prescribe rules and regulations that specifically define circumstances known or reasonably foreseeable to perpetuate bias will require significant technical expertise. The agency should employ and regularly consult with preeminent AI and FRT scholars and researchers so that it can stay abreast of industry standards, norms, and developments. The agency must also develop rigorous testing standards to identify and address algorithms’ rates of bias, which will require it to compile large datasets that are phenotypically and demographically representative.

Part (d) significantly empowers the agency to continually request information from private FRT developers so that it can promulgate rules and standards that can effectively address the identified sources of bias in commercial FRT applications. Without the power to gather and compile data, the agency’s regulations and standards would run the risk of becoming obsolete or irrelevant to the FRT industry, which would hinder its ability to reduce bias. Similarly, the power to conduct investigations related to FRT development, testing, and applications is crucial to the agency’s regulatory authority so that the agency can actively ensure companies’ compliance without needing to wait on injured parties, who often lack AI expertise or access to representation, to bring claims. Based on its investigations, the agency can further ensure the sustained reduction in FRT bias by making reports and recommendations to Congress and the public.

Part (e) can be best realized by the agency because of its broad authority to regulate every aspect of FRT development and application. Thus, the agency sits at a critical juncture between FRT developers, legislators, and the public. Consequently, the agency can emphasize legislative reform as needed to effectively reduce bias and contribute to a nascent body of knowledge that the public has only begun to understand.

A new federal agency, empowered to investigate and regulate FRT development, testing, and application can reduce the presence of bias more effectively than the current regulatory scheme because of its broad authority and enforcement power. The FTC is limited in its authority to regulate bias, and its regulatory power has repeatedly bowed to the will of large corporations. Furthermore, it is not clear whether the Office has the authority to even promulgate rules or standards. Yet, FRT technology is a growing market, and researchers have only scratched the surface of how FRTs perpetuate bias. To this end, the Association of Computing Machinery’s U.S. Technology Policy Committee observed that industry and government have adopted FRTs “ahead of the development of principles and regulations to reliably assure their consistently appropriate and non-prejudicial use” (Association for Computing Machinery [ACM], 2020). A new agency, specifically targeting the development, training, and application of FRTs can have the necessary breadth and expertise to reduce existing sources of bias and discover unknown sources of bias. Furthermore, the agency’s narrowly tailored focus on FRTs can help to lay a foundation for its future expanded regulatory authority over additional AI attendant technologies, which are likely more complex systems. Since large corporations have not dealt with the new agency yet, the agency will be able to set itself apart from agencies with waning respect from corporations by strictly enforcing its regulations, erring on the side of caution, and crafting settlement agreements with provisions that require violators to make material changes to reduce FRT bias. Though the existence of completely unbiased FRTs is sure to be difficult to realize, the new agency will deploy all of its authority and resources to reducing FRT bias to the point of elimination.


The inundation of commercial facial recognition technology coupled with a lagging federal regulatory framework to govern commercial FRT development and use has led to a precarious environment where individuals bear the un- due burden of redressing unprecedented harms. The following policy recommendations, while ambitious, aim to support a national regulatory scheme that would reduce the frequency and severity of FRT bias and discrimination:

  • Establish a federal agency with the explicit authority to regulate commercial AI and its attendant technolo- gies, like FRTs, in accordance with the following enabling statute:

The [agency] is empowered, among other things, to:

(a) prevent private entities’ development, use, or sale of FRTs in circumstances that perpetuate bias based on ethnic, racial, gender, and other human characteristics recognizable by computer systems;
(b) seek monetary redress or other relief for injuries resulting from the presence of bias in FRTs; (c) prescribe rules and regulations defining with specificity circumstances known or reasonably foreseeable to perpetuate bias that is prejudicial to established human and legal rights, and establishing standards designed to prevent such circumstances;
(d) gather and compile data and conduct investigations related to private entities’ development, testing, and application of FRTs; and
(e) make reports and legislative recommendations to Congress and the public.

  • Create uniform guidelines for states’ regulation of the commercial collection and use of biometric data;
  • Develop and encourage the increased implementation of phenotypically and demographically diverse face datasets in commercial FRT development, training, and evaluation.


  1. Khortlan Becton graduated summa cum laude from the University of Alabama with Bachelor of Arts degrees in Religious Studies and African American Studies, received a Master of Theological Studies from Vanderbilt Divinity School, and a Juris Doctor from Temple School of Law. Khortlan is a Truman Scholar Finalist, a member of Phi Beta Kap- pa, and recipient of numerous awards from the various academic institutions that she has attended.
    While attending law school, Khortlan began studying and advocating for the regulation of artificial intelligence technologies, including facial recognition technology. She served as lead author for a summary of recent literature on algorithmic bias in decision-making and related legal implications. That paper’s co-author, Professor Erika Douglas (Temple School of Law), presented the literature review at the American Bar Association’s Antitrust Spring Meeting in 2022.Through holding various service and leadership roles, Becton has developed a deep appreciation for creative and collaborative problem-solving to address intergenerational issues of poverty and systemic inequality. Becton has continued to pursue her passion for education and justice by launching The Restorative Education Institute. The Institute is a non-profit organization purposed to equip youth and adults to practice anti-racism through historical education and substantive reflection.
  2. The FTC, which is playing an active role in the misuse of facial recognition, previously imposed a $5 billion penalty and new privacy restrictions on Facebook in 2019. Similar to the allegations against Everalbum, the complaint against Facebook alleged that Facebook’s data policy was deceptive to users who have Facebook’s facial recognition setting because that setting was turned on by default, while the updated data policy suggested that users would need to opt-in to having facial recognition enabled.
  3. The four large datasets of photographs are: (1) Domestic mugshots collected in the U.S.; (2) Application photographs from a global population of applicants for Immigration benefits; (3) Visa photographs submitted in support of visa applicants; and (4) Border crossing photographs of travelers entering the U.S.
  4. This effect is generally large, with a factor of 100 more false positives between countries.
  5. These differing results relate to image quality: The mugshots were collected with a photographic setup specifically standardized to produce high-quality images across races; the border crossing images deviate from face image quality standards.
  6. The OMB memorandum defines “narrow” AI as “go[ing] beyond advanced conventional computing to learn and perform domain-specific or specialized tasks by extracting information from data sets, or other structured or unstructured sources of information.”
  7. According to Pew Research, Texas is one of two states with the most Latinx people at 11.5 million. Illinois’ Latinx population increased from 2010 to 2019 by 185,000 people.
  8. The new agency’s enabling statute is modeled after the Federal Trade Commission Act because the Act succinctly embodies the power of a narrowly focused agency. The FTC Act is primarily focused on “unfair and deceptive acts or practices affecting commerce,” which has contributed to the FTC’s broad authority. The new agency will need a similar breadth in their jurisdictional scope since researchers have only begun to scratch the surface of bias in FRT applications.
  9. Specifically defining the concepts used to describe the creation and management of FRTs is of utmost importance to delineating the scope of not only the AI attendant tech- nology, but also the breadth of an agency’s regulatory framework. Researchers have recently endeavored to provide specific definitions for the creation of a federal regulatory scheme for FRTs that will likely be a necessary addition to the enabling statutory language proposed here.


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Artificial Intelligence and Automation and Its Potential Impact on Race and Class.

Foreward to the 2023 Hooks Institute Policy Papers

To say the world is in the throes of a technological revolution spearheaded by artificial intelligence (“AI”), and automation, may be one of the most understated observations of this century. While “Fake News” ran rampant on social and other media and influenced the November 2016 presidential election, that election provided ample warning of how media manipulated to mislead can have enormous negative consequences for every segment of life, including personal and employment relationships, national security, elections, media, etc.

However, something is intriguing about AI and automation. It gives us access to a futuristic society allowing us to explore unchartered waters. Bill Gates has argued for years that AI has its proven benefits. Potential uses of AI include creating personalized teaching models for students so that educators can maximize students’ educational experiences (Gates, 2023). “AI can reduce some of the world inequities” (Gates, 2023) through its problem-solving capabilities, enhance worker productivity, and “[a]s computer power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks” (Gates, 2023).

As for the immediate future, AI may create as many casualties as opportunities. Undergirding the Writers Guild of America, strike were Hollywood writers’ concerns that AI, specifically the program ChatGPT (which can produce creative writing and audio in response to prompts), might reduce or eliminate the need for screenwriters in the future (Fortune 2023).

Individuals, governments, and organizations have used AI in insidious ways. In public housing complexes, surveillance cameras create over-policing of people of color. Despite the lack of evidence showing that Facial Recognition Technology (FRT) makes public housing complexes safer, “many of the 1.6 million Americans who live [in public housing] . . . are overwhelming people of color [who are subjected] to round-the-clock surveillance” (MacMillian, 2023). For example, in the small town of Rolette, North Dakota, the public housing complex has 100 residents un- der the surveillance of 107 cameras, “a number of cameras per capita approaching that found in New York’s Riker Island complex” (MacMillian, 2023).

FRT has led to evictions for minor or alleged infractions that have uprooted lives. In Steubenville, Ohio, a resident was evicted for removing a laundry basket from the washing room of the complex, and another was threatened with eviction because she loaned her key fob to an authorized guest (MacMillian, 2023). The latter resident demonstrated that her vision loss required the help of her friend, who brought her groceries, thus successfully pleading

her case against eviction (MacMillian, 2023). A single mother of two in New Bedford, Massachusetts, who received an eviction notice in 2021, stated that the public housing authority “made [her] life hell” when they alleged that
her ex-husband – who was taking care of their children while his former wife worked during the day and attended school at night – was staying in the apartment without contributing rent in violation of the rules (MacMillian, 2023). Even Bill Gates acknowledges that the new frontier of AI is not without rugged and scorched terrain that produces inequities. Gates recognizes that “market forces won’t naturally produce AI products and services that help the poor- est. The opposite is more likely.” He contends that “[w]ith reliable funding and the right policies, governments and philanthropy can ensure that AIs are used to reduce inequity” (Gates, 2023).


The speed with which technology and automation are transforming the landscape is taking place with unprecedented velocity, even outpacing the rate with which changes occurred during the industrial revolution. “The speed of current breakthroughs has no historical precedent. Compared with previous industrial revolutions, the [technological revolution] is evolving at an exponential rather than a linear pace. Moreover, it is disrupting almost every industry in every country. The breadth and depth of these changes herald the transformation of entire systems of production, management, and governance (Schwab, 2015).

This technological revolution has the potential to raise global income levels and improve the quality of life for populations around the world. To date, those who have gained the most from it have been consumers able to afford and access the digital world (Schwab, 2015). By contrast, African Americans, Hispanics, and marginalized people clustered in service,

warehouse, and other low skills occupations are the least likely beneficiaries of AI and automation gains because they are the most susceptible to job loss because of it. (McFerren & Delavega, 2018).

As the nation and world grapple with the societal impact of AI and Automation, the Hooks Institute remains focused on a core question central to promoting justice and equality: what policies and practices will prevent AI and automation from discriminating against people of color and other marginalized groups? How can AI and automation aid our nation in eliminating racial, economic, health, educational, and other disparities?

The policy papers in this edition analyze the impact of AI and automation in three crucial areas. Khortlan Becton, JD, MTS, explores the urgent need to regulate AI to eradicate existing and potential policies and practices that disproportionately discriminate against African Americans and minorities. Becton proposes the creation of a new federal agency to regulate AI.

Susan Elswick, EdD, LCSW, a faculty member at the University of Memphis School of Social Work, seeks a path to using AI and Automation to provide social work counseling to those in need. Elswick not only explores how effective client counseling is dependent upon access and ability to use technology by clients but also argues that social workers require formal training from institutions of higher learning on how to use AI and automation to benefit their clients.

Meka Egwuekwe, MS, founder and executive director of Code Crew, approaches AI and automation from the perspective of a practitioner who teaches others to write computer code. Recognizing that the world is experiencing a revolution in how work is performed, Egwuekwe proposes recommendations that reskill or upskill the workforce, increased support for startups and small businesses, and a societal framework that will embrace universal basic income as a resource to aid those displaced by AI and Automation.

The world has entered the frontier of AI and Automation. Let’s ensure everyone has an equitable opportunity for life, liberty, and the pursuit of happiness as we embark on this evolving and transformative frontier.

Daphene R McFerren, JD
Executive Director, Benjamin L. Hooks Institute for Social Change

Elena Delavega, PhD
Professor, Department of Social Work

Daniel Kiel, JD
FedEx Professor of Law, Cecil C. Humphreys School of Law Editors

June 30, 2023

Read the full 2023 Policy Papers through this link.


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Race and COVID-19: Illuminating Inequities in Education

Young students learning.

By Cardell Orrin (Stand for the Children) and Kelsey Jirikils (Freedom Preparatory Academy).

From the 2021 edition of the Hooks Policy Papers. “Race in the Time of COVID-19.

During a planning period in 2019, I heard school administrators discussing a 7th grader who was exhibiting early signs of a seizure. With no nurse or medical professional, the principal subbed for algebra while the algebra teacher, who fortunately happened to have EMT training, monitored the student. Luckily, the student was fine, but this experience highlighted a glaring issue: our school wasn’t equipped to give students the care they needed.

One of the authors worked at a Title 1 School in Memphis whose student population consisted of over 90% students of color and over 90% low-income students. From school segregation through the 1960s, to White flight in the 1970s, to district secession in 2014, racial disparities existed in Memphis long before COVID-19 (Kiel, 2008). However, the pandemic highlighted that many of the issues in Memphis schools that disproportionately affect students of color can be solved when people in positions of power decide to prioritize them.

Access to technology was an issue in Memphis before the pandemic. In 2019, all six of the municipal districts, which primarily served White students, had either fully implemented or were in the process of implementing a 1:1 initiative (1 device for every student) (Pignolet, 2019). Shelby County Schools (SCS), which primarily serves students of color, lagged behind. While Superintendent Ray proposed a 1:1 initiative soon after he became superintendent, there was not enough support for the proposal to pass with the required funding. As of the fall of 2019, the district had settled on piloting a 1:1 initiative in only nine high schools and making plans to phase the initiative to other schools over six years (Pignolet, 2019). Compared to their White peers, students in SCS were years behind in having access to technology and in learning critical computer skills that would prepare them for post-secondary success.

The pandemic pushed SCS to accelerate their timeline and pushed us as a community to reconsider what was possible. By August of 2020, SCS brokered deals with HP and Microsoft to secure tablets and laptops for Pre-K through 12th graders. These deals were made possible through an influx of money from the CARES Act and other federal funds, the City of Memphis, and other revenue streams identified by Superintendent Joris Ray’s administration (Jaglois, 2020). Of particular note is the five million dollars invested by the City of Memphis. The City relinquished responsibility for funding education in the 2014 fiscal year when their court-ordered mandate was removed with the historic merger of Memphis City Schools and Shelby County Schools (Powell, 2021). While suburban municipalities have significantly increased their direct contributions to education, Memphis is now the only city in Shelby County that does not contribute to K-12 education (Powell, 2021). COVID didn’t create the funding disparity between Memphis and other municipalities; rather, COVID showed us that disparity is a choice. COVID showed us that if the city to invest in public education, then the city can make it happen.

Furthermore, COVID highlighted that education is a community issue. Prior to COVID, teachers were an easy scapegoat for all things wrong in education. COVID showed us that, even if a teacher was doing everything right, if the student and their family didn’t have access to stable housing, food, health care, affordable childcare, and a livable income, we can’t expect learning to happen at its highest level. Due to historic inequities, many low-income families and families of color felt the brunt of the economic downturn and were thus faced with unemployment and housing insecurity (Mitropoulos, 2021). This may have caused some students to start taking on adult responsibilities such as entering the workforce to provide for their families or caring for younger siblings in near fulltime capacities (Mitropoulos, 2021). On top of this, many were dealing with pandemic-related isolation and grief without the support of mental health care. These factors manifested in a substantial rise in chronic absence during the pandemic, particularly for students of color (Mitropoulos, 2021).

It is naive for us to think that a student can come to school and be fully successful while dealing with food insecurity, or working for 20+ hours a week out of necessity, or watching their parents stress about finding work and affordable housing. If we care about the children in our community getting a quality education, then we need to create conditions that ensure each student can be physically and mentally present to receive an education. That means making housing security a priority, making childcare affordable, making sure single parents can support their families without children needing to work to make household ends meet. COVID showed us, in an intensified state, that when we ignore the interconnectedness of these issues, we do a disservice to children, families, and our community’s future. The COVID pandemic has also shown us that when we put our collective commitment and resources (local, state, and federal) towards addressing challenging situations, we can identify solutions and put them in place.

The pandemic has laid bare that as a society, we have been failing to support the holistic needs of our students, especially those from economically disadvantaged backgrounds and students of color. In the classroom, we were allowing young people with the greatest needs to fall behind their peers in access to technology, mental health supports, and resources to support their academic achievement. Outside the classroom, we were failing to establish systems to support their families with access to housing, food, and extended financial resources. COVID didn’t cause these issues, but the pandemic has made them more apparent. During this pandemic, we have identified resources to support student education, mental health, housing, food access, and financial payments for families. It has been made readily clear that if we want to effect change, we can make that happen and the only thing stopping us is the will and courage.


The pandemic has laid bare that as a society, we have been failing to support the holistic needs of our students, especially those from economically disadvantaged backgrounds and students of color. While ambitious and requiring national and local support, the following policy recommendations would alleviate the crisis children and families face:

  • A guaranteed minimum income for poor families which would help address housing and food insecurities; Universal health care for children and adults;
  • Increase equitable funding to schools with the purpose of improving compensation to attract and retain more highly qualified educators and support staff within schools, along with resources targeted to literacy, social-emotional supports, and high school success.
  • Expand community schools that identify needs and connect students and their families to the resources and opportunities that will support them to thrive in education and life. This includes the recognition that these are not just school and district responsibilities and should involve investments and resources from local, state, and federal governments and agencies.
  • Permanent funding to bridge the ongoing digital divide for under-resourced families that will continue in the future even after the end of the COVID-19 pandemic.


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  • divide. Action News 5 [Memphis]. Retrieved from investigators-breaking-down-cost-bridging-shelby-countys-digital-divide/Powell, M. (2021, June
    15. Memphis’ budget needs to redirect funds to empower and uplift our students. Memphis Commercial Appeal. Retrieved from education-hurts-memphis-students-and-families/7698480002/
  • Mitropoulos, A. (2021, March 21). Thousands of students reported ‘missing’ from school systems nationwide amid COVID-19 pandemic. ABC News, Retrieved from eported-missing-school-systems-nationwide-amid/story?id=76063922

Watch the lecture on “Race and COVID-19: Illuminating Inequities in Education” on our YouTube page.

COVID-19 and Evictions in Memphis

By Andrew Guthrie, PhD (Assistant Professor, City and Regional Planning The University of Memphis), Courtnee Melton-Fant, PhD (Assisant Professor, Division of Health Systems Management The University of Memphis), and Katherine Lambert-Pennington, PhD (Associate Professor, Department of Anthropology The University of Memphis).

From the 2021 edition of the Hooks Institute Policy Papers “Race in the Time of COVID-19.”


The COVID-19 pandemic has exacerbated existing racial inequalities in employment, financial security, and health access, and the economic repercussions have been disproportionately shouldered by women (Jin et al. 2021). Nationally and locally, Black, Latinx, and Asian people have higher rates of COVID-19 morbidity and mortality compared to White people (Lopez, Hart, & Katz, 2021). Structural racism – the intersecting and reinforcing policies, systems, and institutions that create advantages and disadvantages based on race (Bailey & Moon, 2020) – have resulted in racial disparities. Nowhere is this more evident than in the housing sector. The pandemic amplified housing insecurity, as millions of people lost income, jobs and dealt with COVID related health challenges and deaths.

Housing insecurity, lack of access to safe, affordable, and stable housing, disproportionally impacts communities of color. Black and Latino families have lower rates of homeownership, live in more segregated neighborhoods, pay more for housing, and have been at greater risk of foreclosure than White homeowners. Further, Black and Latinx households are more likely to be renters than White households; they also face evictions at a much higher rate (Greenberg et al., 2016). Given income and job loss, Benfer et al. (2020) estimate that 30-40 million renters are at risk of eviction. To help mitigate this risk and stem the likelihood of COVID transmission (Nande et al., 2021; Jowers et al., 2021), the federal government imposed a national moratorium on evictions recommended by the Center for Disease Control (CDC) and provided $25 billion dollars to states and local governments to fund emergency rental assistance.

Research has shown that the national mortarium on eviction hearings and decisions was effective in slowing evictions and allowed households to use financial resources to meet immediate needs (An et al., 2021). However, as the economy slowly recovers, and enhanced federal unemployment benefits end, the long-term impact of the pandemic on housing security is likely to be devastating. While data is not fully available, two key indicators of housing affordability- income and the proportion of income to rent cost, often referred to as cost-burden- serve as important determinants of a household’s risk for eviction. Additionally, racial disparities in housing security and employment in essential worker roles, and vulnerability to COVID-related job loss are crucial to understanding what policy steps would be most effective to address the impending housing crisis in Memphis. Manifestations of structural racism that are particularly relevant for Memphis include racial residential segregation, the proliferation of housing insecurity in Black neighborhoods, and the overrepresentation of Black and Latinx workers in the service industry.

Early Indications of Pandemic Effects

With one of the highest rates in the nation, evictions in Memphis have been an acute problem for years. Despite a state eviction moratorium in the spring and summer of 2020 and the CDC order, which was extended until October 3, 2021, eviction filings in Memphis have continued. Over eighteen thousand evictions have been
filed since the start of the pandemic and are continuing at a rate of between 200 and 300 per week (Princeton University, 2021).

Systematic analysis of the effects of COVID-19 on the housing sector is complicated by the ongoing, dynamic nature of the pandemic and the 1- to 2-year time lag in availability of most sources of social data at the fine geographic scales needed to fully understand the social and spatial dynamics at play in Memphis. In particular,
the lack of unemployment data at less than county scale obscures a crucial link in the chain of events most likely to lead to an eviction as a result of COVID: pandemic-related job loss leading to an inability to make rent. In the interest of providing as much timely information to policymakers and the public as possible, proxy measures—such as residential locations of workers in sectors especially likely to experience job loss—can approximate unavailable data.


The table below shows specific measures of COVID’s implications for housing in Memphis, as well as definitions of each measure and data sources. These measures consider vulnerability to eviction directly via pre-pandemic housing unaffordability and susceptibility to job loss as well as in a context of structural inequality and historic marginalization.




Rent-Burdened Households

% of households paying >30% of monthly income in rent

American Community Survey (2015-2019)

Black Residents

% of population who self-ID as “Black or African American”.

American Community Survey (2015-2019)

Service Workers

Number of workers in the Retail Trade, Accommodation and Food Service and Arts, Entertainment and Recreation sectors

Longitudinal Employer and Household Dynamics (LEHD) Database (2018)


Number of legal evictions recorded by the county. Expressed both as a count and as the ratio of evictions to rental households

Shelby County Housing Court (via Innovate Memphis); American Community Survey

Geospatial Analysis

Although this data is preliminary, strong spatial relationships exist in Memphis between key measures of social marginalization and economic vulnerability and the prevalence of evictions in 2020. The following four maps show these measures as densities. Density maps allow us to explore where in the city the greatest numbers of people experience eviction, housing insecurity and other social factors which increase vulnerability to both. For each map, we select a social condition to explore—i.e. paying more than 30% of one’s household income in rent, being a service worker before the pandemic or being evicted—count the number of times that condition occurs within a quarter-mile grid, and use a heat-map algorithm to smooth the result into continuous gradients based on surrounding squares’ values. All five maps use a quintile scale, with the darkest gray squares showing areas in the 80th percentile or above, next darkest the 60th-80th percentile, etc. This mapping approach allows us to see patterns of social disparities from one neighborhood to another while also focusing on neighborhoods with relatively the most intense housing injustices.

Map 1

Map 2

The second map (above) shows concentrations of Black residents. Memphis is a racially segregated city, as can be seen by how tightly concentrated the Black population is, compared even with the rent-burdened population. (i.e., Memphis is still a highly spatially segregated city, with the vulnerabilities of rent burden and service-industry employment compounded by historic disinvestment and structural racism). Note, however, that concentrations of Black residents do follow the most intense concentrations of rent burdened households quite closely.

The third map (below) shows concentrations of where workers in the retail, sales, hospitality, food service and entertainment industries lived before the pandemic. County-level data indicate workers in these sectors were disproportionately likely to have suffered job loss. Note again the general similarity with the preceding maps, with the degree of concentration falling between Black residents and rent-burdened households. In particular, the spatial relationship between workers likely to have lost jobs and households already facing unaffordable rents beforehand shows the susceptibility of their neighborhoods to an economic and health shock like COVID.

Map 3

The final two maps (page 8) show concentrations of evictions, both in absolute terms (for consistency with the preceding maps) and weighted by the number of renter households in each census block group (for consistency with standard measures in the housing field). Eviction densities do not show all the housing precarity or injustice in a neighborhood, but they do represent a rapidly-available, geographically precise measure of extreme housing injustice due to legal filing requirements. Though the scale of the (upper) absolute map is somewhat dominated by a single, intense cluster of evictions to the southwest, the overall spatial pattern is both stark and by this point familiar, tracking those of Black residents and service workers especially closely. The most intense areas of the (lower) weighted eviction density map show a largely similar shape to those of the absolute map, but do not stand out as strongly from their surroundings, likely due to smaller numbers of renter households in wealthier and/or suburban areas. It is important to note, however, that this final map shows evictions are a problem county-wide and may only appear not to be in outlying areas due to lower densities of renters.

Map 4

Map 5

We can see from these maps that the highest rates of evictions in Shelby County have a strong spatial relationship to long-standing patterns of structural inequality—particularly in the case of the unweighted map. However, the weighted map shows us that evictions are a problem throughout Shelby County in the context of an individual renter household’s likelihood of being evicted, though it is crucial to note that patterns of structural inequality still appear in the weighted map, even accounting for inter-neighborhood differences in numbers of renters. In other words, though a robust policy response is required throughout the county, special focus must be placed on neighborhoods affected by structural racial and economic inequality. Finally, the close spatial correspondence between eviction rates and pre-COVID rent burden shows that evictions are both an acute problem and a chronic one: the pandemic did not create a crisis where there was none before; in large part it seems to have pushed households who were already struggling over the edge. Understanding this does not change the need for rapid, emergency assistance to Memphians facing eviction, but it does also call for a longer-term policy response to ongoing issues of housing unaffordability and insecure tenure.

The COVID-19 pandemic exacerbated the existing housing crisis in Memphis, but the full effect of housing insecurity on eviction rates and neighborhood stability have yet to be fully revealed. The ongoing housing crisis in Memphis and the COVID-19 pandemic require multifaceted policy solutions that not only respond to immediate needs but also address the larger housing affordability issues in the city. While policy interventions are needed at all levels of government, we focus our recommendations on state and local level policies that are most relevant to the Memphis context. As shown in Figure 1, Memphis is already implementing eviction prevention and mitigation policies and working to increase housing stability.

Figure 1. Policy levers for improving housing stability

* Denotes policies and programs that are currently being utilized in Memphis


  • ŸIncrease outreach and education about the Emergency Rental Assistance Program (ERA)
    The Emergency Rental Assistance Program (ERA), funded through the CARES Act and administered in Memphis by United Housing, provides eviction settlement funds to households who have suffered a finan- cial loss due to COVID-19 and live on less than 80% of their county’s median income. A total of 1,320 Shelby County residents received rental assistance in June. Though data are only available at the state level, the Census Bureau’s most recent Household Pulse Survey estimates 84,447 Tennessee households fear being evicted in the next two months. Proportional to population, Shelby County’s share of that total would be 11,593—over ten times the number currently being helped—even ignoring our high rates of pov- erty and structural inequality. Though funding is available to help significantly more households, difficulties in applying and obtaining cooperation from landlords have reduced numbers served.

    • Providing additional community outreach and education about the program and direct assistance in applying as well as encouraging landlords to participate as strongly as permitted by law are im- portant steps to ensure Memphians who could be helped are not needlessly evicted.
    • In addition, though CARES Act funds are limited to renters making less than 80% of the Area Median Income (AMI), roughly 15% of households earning 81-100% AMI in Shelby County make more than that, but not enough to afford a median rental cost (National Low Income Housing Coalition, 2021). These households may face additional risk of eviction due to the “benefit cliff” coming at an income low enough to still render most housing unaffordable. Other options should be explored for providing assistance to households facing eviction who fall outside CARES Act eligibility requirements, as a means of funding unmet needs while, crucially, holding those renter households most in need harmless.
  • Provide sustainable infrastructure and funding for the Eviction Settlement Program (ESP)
    The ESP is currently being funded with federal CARES Act dollars to provides tenants with legal assistance and mediation when they are behind on their rent. This program relies on volunteer attorneys and mediators and could provide more assistance to tenants if they had more resources. The services provided by the ESP are critical for preventing evictions and preserving affordable housing (Benfer et al, 2020, Sabbeth 2018).
  • Enact laws at the state and local level to prevent evictions and lessen the negative downstream effects
    • Tenants who are represented by attorneys are less likely to be evicted (Sabbeth, 2018), but Ten- nesseans do not have a right to counsel in eviction cases because eviction proceedings are civil actions, not criminal matters. Right to counsel laws can ensure that tenants have representation during eviction proceedings.
    • Having an eviction or eviction filing on one’s record makes it more difficult to find housing because many landlords do not want to rent to them. Eviction record sealing and eviction expungement laws can improve tenants’ access to housing following an eviction or eviction filing (Fleurant, 2020).
  • Increase investment in historically underserved communities that were disproportionately affected by both COVID-19 and housing instability
    Memphis needs an estimated 30,000 affordable housing units (Innovate Memphis, 2020) and is using multiple levers to address this gap including the establishment of the Memphis Affordable Housing Trust Fund (MAHTF) and the Memphis 3.0 Plan to guide investment and land use regulation in the creation of healthy affordable communities. However:

    • The MAHTF is underfunded compared to peer cities and funding for 2021 was not included in the budget because of COVID-19 (BLDG Memphis, 2020).
    • Memphis has comparatively low capital investment that is segregated by race and poverty (Theo- dos et al, 2021). The Memphis 3.0 plan is the city’s comprehensive approach to equitably develop and invest in the city. Time will tell if the plan will be able to overcome historical and longstanding patterns of disinvestment and policy that have contributed to the current housing crisis.
  • Stronger enforcement of existing laws
    In addition to affordability issues, many Memphians live in substandard housing conditions that are harmful to their health. Like many other states, Tennessee has laws requiring landlords to maintain their properties and provide habitable conditions for tenants. Yet, these laws are not always enforced, and tenants may not be aware of these laws (Sabbeth, 2018). Enforcing these laws is necessary for increasing the supply of affordable, healthy housing and keeping tenants in their homes.
  • Increase vaccination access and uptake in structurally vulnerable communities
    Recent research has shown that neighborhoods with higher eviction filing rates have lower vaccination rates indicating that the higher risk of evictions and of contracting and passing COVID-19 are spatially con- centrated. Place-based interventions, tailored to the specific concerns and desires of these communities, are needed.

Interested in more? Watch the lecture of “COVID-19 and Evictions in Memphis” on the Hooks Institute YouTube page.


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Hooks Policy Papers: Race in the Time of Covid

As the world confronted the pandemic unleashed by COVID-19, new language emerged. “Social distance” transformed from Georg Simmel’s concept referring to social relationships between racial, gender, and economic groups to the 6-foot physical distance vital for stopping the virus spread. Concepts like “isolation” and “quarantine” took on new meaning. People grew comfortable with medical terms like “asymptomatic” or “incubation period.”

Yet, even as we faced an unprecedented and deadly global test, tragically familiar and stubbornly persistent disparities were amplified by the encounter with the pandemic. Alongside the new vocabulary, familiar concepts reasserted their relevance in phrases like “racial inequality,” “housing insecurity,” and “health disparities.” While these societal failures have always demanded action, the crucible of the pandemic has even more directly made them matters of life and death.

The COVID-19 pandemic has affected everyone, but it has certainly not affected everyone equally. Preexisting conditions in our nation’s communities have ensured that those already most vulnerable to depressed economic, educational, and health conditions were impacted the most. In the healthcare field, “social determinants of health” have emerged in recent years as a powerful way of connecting disparities in health to social inequities that exacerbate those disparities. In Memphis and Shelby County, as elsewhere, the roots of the unequal impact of COVID-19 can be found in inequalities that long predate the outbreak of the disease. Our community’s social determinants of health have amplified the effects of the pandemic on our most vulnerable neighbors.

This issue of the Hooks Institute Policy Papers addresses the varied ways COVID-19 has magnified and worsened racial and socioeconomic disparities in Shelby County and other communities. Beginning with housing, educational, and employment effects, and concluding with health disparities and the impact of COVID-19 mortality disparities on the preservation of wealth, each writer connects preexisting social circumstances to the travails of the pandemic. Offering a wide range of expertise, the papers recommend short-term interventions to the acute crises brought on by the pandemic and long-term preventative changes to address the underlying social deficiencies.

In “COVID-19 and Evictions in Memphis,” Andrew Guthrie, Courtnee Melton-Fant, and Katherine Lambert-Pennington provide staggering spatial representations of social marginalization and economic vulnerability in Shelby County, focusing on susceptibility to evictions. They note the ways in which the pandemic amplified housing insecurity but observe that the pandemic did not create that crisis; rather, it merely pushed those already struggling over the edge. They further note that with the removal of pandemic-related protections, evictions are likely to increase the deterioration of circumstances for the county’s most economically vulnerable, a group made up disproportionately of African Americans.

In “Race & COVID-19: Illuminating Inequities in Education,” Cardell Orrin and Kelsey Jirikils highlight how the pandemic more clearly revealed the vast disparities in resources available to students throughout Shelby County. Of note, as schools moved to virtual learning, disparities in access to technology ensured that some students would have difficulty in even accessing education at all. Further, despite increased needs due to the social isolation and trauma of the pandemic, students were unable to access mental health services that would have strengthened their ability to get the most out of schooling.

Elena Delavega and Gregory M. Blumenthal build on these themes in “COVID-19 and Work: Employment Disparities Magnified,” where they quantify the ways in which the pandemic’s work disruptions fell most harshly on the most vulnerable, again, a group made up disproportionately of racial and ethnic minorities. The pandemic exposed a divide in who could work from home (and thus maintain employment, health care, and oversee children in virtual school) and who could not. The authors critique the fact that workers deemed “essential” in terms of providing services for the more privileged were not provided protections and salaries consistent with such “essential” status.

In “The Power of Will – And Its Limits,” Daniel Kiel provides a slightly different perspective by examining the emergency policy responses to the pandemic’s most urgent social needs. A mortarium on evictions, free provision of technology for students, and expanded unemployment benefits were not new ideas when the pandemic arrived, but it took the shocks of COVID-19 to make them viable policy options. To Kiel, this demonstrated that solutions to longstanding social problems are possible, but only where there is sufficient public will and need, something that will be difficult to maintain as the pandemic subsides, but that is no less urgent.

Turning more directly to the health impacts of COVID-19, Albert Mosley discusses the social determinants of health in the age of the pandemic in “Through a Glass Darkly: Musings on the Harsh Realities of COVID-19.” Highlighting racial disparities in hospitalizations, mortalities, and vaccination rates, Mosley laments that such distressing statistics were entirely predictable given this community’s history with systemic racism which has perpetuated economic and educational disparities. In addition to bearing shortcomings within the healthcare system, COVID-19 provided a harsh mirror to the broader community on the topic of providing wellness, the most basic of human needs.

Finally, in “Life After Death: COVID-19’s Impact on the Wealth of African American Families,” Daphene McFerren describes the deterioration of wealth that results when individuals pass away without a will or proper direction as to how to distribute their estate, a problem made tragically more vital during the pandemic. Urging more attention to estate planning in the African American community, McFerren pushes for greater access to legal resources and a shift in community attitudes in order to stop the massive racial gap in net worth from growing even larger due to a loss of intergenerational wealth.

Cumulatively, these papers examine the ways in which the COVID-19 pandemic augmented some of society’s most obstinate problems and each display how these problems are interconnected. While the pandemic has brought much suffering and further social division, it has also provided an undeniable perspective on the urgency of these lingering social problems. The recommendations here provide a starting point for meaningful discussions and effective treatment.

Daphene McFerren, JD Executive Director, Benjamin L. Hooks Institute for Social Change the University of Memphis

Elena Delavega, Ph.D. Professor, Department of Social Work the University of Memphis

Daniel Kiel, JD Associate Director, Benjamin L. Hooks Institute for Social Change FedEx Professor of Law, Cecil C. Humphreys School of Law the University of Memphis

Read the policy papers here.