Challenging the Use of Algorithm-driven Decision-making in Benefits Determinations Affecting People with Disabilities

Brown, Lydia X. Z.; Richardson, Michelle; Shetty, Ridhi; Crawford, Andrew

Difficult-to-understand algorithm-driven decision-making tools are often implemented to assess people's eligibility for, or the distribution of, public benefits. They frequently reduce and deny benefits, though, often with unfair and inhumane results. This report analyzes legal challenges to these tools that have been filed within the past 10 years. It also identifies key insights into what went wrong, and analyzes the legal arguments that plaintiffs have used to challenge those systems in court. 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. 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.