This hub introduces the UK government's Algorithmic Transparency Recording Standard (ATRS), a structured framework for public sector bodies to disclose how they use algorithmic tools in decision-making.
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.
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 explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
