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59 Ct. Rev. 10 (2023)
The Dilemma of Black Coding: Assessing Algorithmic Discrimination Legislation in the United States

handle is hein.journals/ctrev59 and id is 10 raw text is: The Dilemma of Black Coding: Assessing Algorithmic Discrimination Legislation in the United States Clarence Okoh The proliferation of algorithmic harms has become one of the most urgent civil and human rights challenges in the United States. Emerging technologies such as artificial intelligence, machine learning and similar algorithmic-driven technologies are used to regulate access to essential goods and services including housing, employment, healthcare, public benefits, financial ser- vices, and education.' These technologies are also routinely used to influence judicial decision-making, expand police surveil- lance, determine parental rights, and facilitate immigration enforcement among other rights-impacting activities.2 In each of these domains, the introduction of algorithmic technologies have facilitated systemic civil and human rights abuses that are often focused on Black communities and other racially marginalized groups.3 It is increasingly clear that the concentrated impact of algorithmic harms on racially marginalized communities threat- ens to erode essential democratic values and norms by reinforc- ing historic patterns of racial hierarchy through systemic civil and human rights violations.4 In recent years, algorithmic harms have captured the attention of lawmakers in the United States interested in developing com- prehensive legal protections to address these challenges. Nascent reform efforts have largely coalesced around a legislative frame- work that centers algorithmic auditing - a periodic technical evaluation of algorithmic systems by private entities to determine whether the technology facilitates adverse impact However, the challenge is that algorithmic auditing is a relatively new and largely unregulated field which relies on technical methods that often fail to holistically assess the full spectrum of racial justice implications of emerging technologies. In fact, in some instances, algorithmic auditing can reinforce racialized harms through the privatization of civil and human rights law enforcement. More specifically, audit-centric legislative reforms can be a poor fit to address a challenge that this paper will refer to as black coding. Black coding is a term I use to refer to any sociotechnical practice where algorithmic technologies are used to evade, displace, or erode existing civil and human rights pro- tections in ways that structure racial hierarchy and social inequality Black coding is present when public and private enti- ties rely on algorithmic decision-making systems to determine the legal rights, status, and freedoms of marginalized communi- ties in ways that contravene existing civil and human rights norms. In this sense, black coding fits within a broader historical pattern where the absence of rigorous public oversight and democratic governance facilitates the erosion of fundamental rights for Black people and other racially marginalized commu- nities in the United States. Left unchecked, the cumulative effect of black coding can result in a collection of practices that effec- tively return racially marginalized communities to second-class citizenship in American life. This article proceeds in three parts. Part I provides a general overview of nascent legislative proposals designed by U.S. law- makers to address algorithmic discrimination. Part II explores black coding in more detail by comparing these practices to 19th Black Codes to demonstrate the insufficiencies of audit-cen- tric legislative proposals. Part III concludes by outlining the fol- lowing three guiding principles to aid policymakers in crafting solutions that address black coding, algorithmic discrimination, and other racialized algorithmic harms. First, emerging technolo- gies used in the context of fundamental rights should be subject to agency preauthorization requirements. A preauthorization regime would require that covered entities receive public autho- rization prior to deployment of any algorithmic technology used in the context of fundamental rights and freedoms. Second, pro- posals should adopt global standards and best practices related to algorithm fairness and antidiscrimination. Third, reparative justice and transformative justice should be integrated into legisla- tive remedial schemes to address the systemic impact of racial- ized algorithmic harms. NASCENT LEGISLATIVE RESPONSES TO ALGORITHMIC DISCRIMINATION IN THE UNITED STATES In recent years, policymakers and advocates have led nascent Footnotes 1. See e.g., Julia Angwin et. al., Machine Bias, ProPublica (23 May 2016), https://www.propublica.org/article/machine-bias-risk- assessments-in-criminal-sentencing; Manish Raghavan & Solon Barocas, Challenges for mitigating bias in algorithmic hiring, Brookings (6 December 2019), https://www.brookings.edu/ research/challenges-for-mitigating-bias-in-algorithmic-hiring/; Kate Crawford et. al., AI NOW 2019 Report, Al NOW Institute (December 2019), https://ainowinstitute.org/AINow_ 2019_Report.pdf 2. See e.g., Kesha Moore, Pretrial Justice Without Money Bail or Risk Assessments, Thurgood Marshall Institute (January 2022), https ://tminstituteldf. org/wp-content/uploads/2 022/01/ TMIPretrialJusticeWithoutCBorRA2.pdf 3. See e.g., Data Capitalism and Algorithmic Racism, Demos (17 May 2021), https://www. demos. org/research/data-capitalism-and- algorithmic-racism; Automating Banishment: The Surveillance and Policing of Looted Land STOP LAPD Spying Coaltion, (November 2021), https://automatingbanishment.org/; #NoTechForICE, NotechForICE (last accessed March 1, 2022), https://notechforice. cor/ 4. See e.g., Safiya Umoja Noble, Algorithms of Oppression (NYU Press 2018); Ruha Benjamin, Race After Technology (Polity Press 2019); Rashida Richardson, Jason Schultz, Kate Crawford Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predic- tive Policing Systems, and Justice, New York University Law Review, Vol. 94, No. 15 (May 2019). 5. Mona Sloane, The Algorithmic Auditing Trap, OneZero (17 March 2021), https://onezero.medium.con/the-algorithmic-auditing-trap- 9a6f2d4d461d; Inioluwa et. al., Closing the AT Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Audit- ing, Arixiv:200l.00973 [cs.CYI (3 January 2020). 10 Court Review - Volume 59 I This article was originally published by Transatlantic Policy Quarterly, and is reprinted with its hind permission.

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