Zollo et al: Towards Effective Discrimination Testing for Generative AI #FAccT2025

Wang et al: Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways #FAccT2025

Panigutti et al: How to investigate algorithmic-driven risks in online platforms and search engines? A narrative review through the lens of the EU Digital Services Act #FAccT2025

Sandoval and Jing: Historical Methods for AI Evaluations, Assessments, and Audits #FAccT2025

Gerchick et al: Auditing the Audits: Lessons for Algorithmic Accountability from Local Law 144's Bias Audits #FAccT2025

Harvey et al: A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms #FAccT2025

De et al: Who Gets Heard? Calling Out the "Hard-to-Reach" Myth for Non-WEIRD Populations' Recruitment and Involvement in Research #FAccT2025

Urman et al: WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms - A Systematic Literature Review #FAccT2025

Safir et al: Distributive Epistemic Injustice in AI Ethics: A Co-productionist Account of Global North-South Politics in Knowledge Production #FAccT2025

Schroeder et al: Disclosure without Engagement: An Empirical Review of Positionality Statements at FAccT #FAccT2025

Lepp and Smith: "You Cannot Sound Like GPT": Signs of language discrimination and resistance in computer science publishing #FAccT2025