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 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.
The study investigates how state agencies administering SNAP comply with Title VI of the Civil Rights Act by providing language access for individuals with limited English proficiency (LEP).
This report offers a detailed assessment of how AI and emerging technologies could impact the Social Security Administration’s disability benefits determinations, recommending guardrails and principles to protect applicant rights, mitigate bias, and promote fairness.
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
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.
This article examines how Chile’s SUSESO is balancing cost-focused procurement criteria with ethical AI concerns in its medical claims automation process.
This review evaluates the UK public sector's use of digital technology, identifying successes and systemic challenges, and proposes reforms to enhance service delivery.
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.
