Governments are increasingly turning to algorithms to determine whether and to what extent people should receive crucial benefits for programs like Medicaid, Medicare, unemployment, and Social Security Disability. Billed as a way to increase efficiency and root out fraud, these algorithm-driven decision-making tools are often implemented without much public debate and are incredibly difficult to understand once underway. Reports from people on the ground confirm that the tools are frequently reducing and denying benefits, often with unfair and inhumane results.
Benefits recipients are challenging these tools in court, arguing that flaws in the programs’ design or execution violate their due process rights, among other claims. These cases are some of the few active courtroom challenges to algorithm-driven decision-making, producing important precedent about people’s right to notice, explanation, and other procedural due process safeguards when algorithm-driven decisions are made about them. As the legal and policy world continues to recognize the outsized impact of algorithm-driven decision-making in various aspects of our lives, public benefits cases provide important insights into how such tools can operate; the risks of errors in design and execution; and the devastating human toll when tools are adopted without effective notice, input, oversight, and accountability.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court. It draws on direct interviews with attorneys who have litigated these cases and plaintiffs who sought to vindicate their rights in court – in some instances suing not only for themselves, but on behalf of similarly situated people. The attorneys work in legal aid offices, civil rights litigation shops, law school clinics, and disability protection and advocacy offices. The cases cover a range of benefits issues and have netted mixed results.
People with disabilities experience disproportionate and particular harm because of unjust algorithm-driven decision-making, and we have attempted to center disabled people’s stories and cases in this paper. As disabled people fight for rights inside and outside the courtroom on a wide range of issues, we focus on litigation and highlight the major legal theories for challenging improper algorithm-driven benefit denials in the U.S.
The good news is that in some cases, plaintiffs are successfully challenging improper adverse benefits decisions with Constitutional, statutory, and administrative claims. But like other forms of civil rights and impact litigation, the bad news is that relief can be temporary and is almost always delayed. Litigation must therefore work in tandem with the development of new processes driven by people who require access to public assistance and whose needs are centered in these processes. We hope this contribution informs not only the development of effective litigation, but a broader public conversation about the thoughtful design, use, and oversight of algorithm-driven decision-making systems.
Find the full report here.
Find the plain language version of the report here.
Find a screen reader-accessible version of the chart here [doc].
Watch the webinar launch of this paper here.