This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.
In this policy brief and video, Michele Gilman summarizes evidence-based recommendations for better structuring public participation processes for AI, and underscores the urgency of enacting them.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
This report analyzes the growing use of generative AI, particularly large language models, in enabling and scaling fraudulent activities, exploring the evolving tactics, risks, and potential countermeasures.
This report offers a critical framework for designing algorithmic impact assessments (AIAs) by drawing lessons from existing impact assessments in areas like environment, privacy, and human rights to ensure accountability and reduce algorithmic harms.
