This report investigates how D.C. government agencies use automated decision-making (ADM) systems and highlights their risks to privacy, fairness, and accountability in public services.
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
This policy brief explores how federal privacy laws like the Privacy Act of 1974 limit demographic data collection, undermining government efforts to conduct equity assessments and address algorithmic bias.
This resource helps individuals with aligning their work with the needs of the communities they wish to serve, while reducing the likelihood of harms and risks those communities may face due to the development and deployment of AI technologies.
An academic research paper introducing SHADES, a multilingual benchmark designed to evaluate how large language models (LLMs) generate and reinforce stereotypes across different languages and cultural contexts.
A policy brief outlining concrete actions states can take to regulate tenant screening practices and reduce harm from inaccurate reports, automated scoring, and discriminatory impacts in the rental housing market.
The Electronic Privacy Information Center (EPIC) emphasizes the necessity of adopting broad regulatory definitions for automated decision-making systems (ADS) to ensure comprehensive oversight and protection against potential harms.
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.
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
This report by EPIC investigates how automated decision-making (ADM) systems are used across Washington, D.C.’s public services and the resulting impacts on equity, privacy, and access to benefits.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
