This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
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 article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
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.
The AI Insights publication series from the UK Government’s Government Digital Service (GDS) provides technical guidance to help public sector organisations understand, implement, and manage artificial intelligence systems safely and effectively.
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.
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.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
A recap of a community innovation hackathon in Seattle where technologists and students used AI to prototype solutions that help youth discover and access local programs and services.
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.
