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 study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
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 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 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.
This article examines how Chile’s SUSESO is balancing cost-focused procurement criteria with ethical AI concerns in its medical claims automation process.
Guidance outlining how Australian government agencies can train staff on artificial intelligence, covering key concepts, responsible use, and alignment with national AI ethics and policy frameworks.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
A panel of experts discuss the application of civil rights protections to emerging AI technologies, highlighting potential harms, the need for inclusive teams, and the importance of avoiding technology-centric solutions to social problems.
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.
A unified taxonomy and tooling suite that consolidates AI risks across frameworks and links them to datasets, benchmarks, and mitigation strategies to support practical AI governance.
