| 2026 | ACL | Multi-lingual Functional Evaluation for Large Language Models. | Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian |
| 2025 | AISTATS | Evaluating Prediction-based Interventions with Human Decision Makers In Mind. | Inioluwa Deborah Raji, Lydia T. Liu |
| 2025 | CHI | Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling. | Victor Ojewale, Ryan Steed, Briana Vecchione, Abeba Birhane, Inioluwa Deborah Raji |
| 2025 | ICML | Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity. | Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini, Shiladitya Dutta, Frances Dean, Inioluwa Deborah Raji, Travis Zack |
| 2025 | ICML | From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms. | Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht |
| 2023 | CHI | Accountability in Algorithmic Systems: From Principles to Practice. | Daricia Wilkinson, Kate Crawford, Hanna M. Wallach, Inioluwa Deborah Raji, Bogdana Rakova, Ranjit Singh, Angelika Strohmayer, Ethan Zuckerman |
| 2022 | AIES | From Algorithmic Audits to Actual Accountability: Overcoming Practical Roadblocks on the Path to Meaningful Audit Interventions for AI Governance. | Inioluwa Deborah Raji |
| 2022 | AIES | Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance. | Inioluwa Deborah Raji, Peggy Xu, Colleen Honigsberg, Daniel E. Ho |
| 2020 | AIES | Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing. | Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton |
| 2019 | AIES | Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. | Inioluwa Deborah Raji, Joy Buolamwini |