Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. - Inioluwa Deborah Raji; Joy Buolamwini (AIES, 2019)

Model Cards for Model Reporting. - Margaret Mitchell; Simone Wu; Andrew Zaldivar; Parker Barnes; Lucy Vasserman; Ben Hutchinson; Elena Spitzer; Inioluwa Deborah Raji; Timnit Gebru (FAT, 2019)

On the Legal Compatibility of Fairness Definitions. - Alice Xiang; Inioluwa Deborah Raji (CoRR, 2019)

ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles. - Inioluwa Deborah Raji; Jingying Yang (CoRR, 2019)

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing. - Inioluwa Deborah Raji; Timnit Gebru; Margaret Mitchell; Joy Buolamwini; Joonseok Lee; Emily Denton (AIES, 2020)

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. - Inioluwa Deborah Raji; Andrew Smart; Rebecca N. White; Margaret Mitchell; Timnit Gebru; Ben Hutchinson; Jamila Smith-Loud; Daniel Theron; Parker Barnes (FAT*, 2020)

The Discomfort of Death Counts: Mourning through the Distorted Lens of Reported COVID-19 Death Data. - Inioluwa Deborah Raji (Patterns, 2020)

Handle with Care: Lessons for Data Science from Black Female Scholars. - Inioluwa Deborah Raji (Patterns, 2020)

You Can't Sit With Us: Exclusionary Pedagogy in AI Ethics Education. - Inioluwa Deborah Raji; Morgan Klaus Scheuerman; Razvan Amironesei (FAccT, 2021)

AI and the Everything in the Whole Wide World Benchmark. - Inioluwa Deborah Raji; Emily Denton; Emily M. Bender; Alex Hanna; Amandalynne Paullada (NeurIPS Datasets and Benchmarks, 2021)

About Face: A Survey of Facial Recognition Evaluation. - Inioluwa Deborah Raji; Genevieve Fried (CoRR, 2021)

Data and its (dis)contents: A survey of dataset development and use in machine learning research. - Amandalynne Paullada; Inioluwa Deborah Raji; Emily M. Bender; Emily Denton; Alex Hanna (Patterns, 2021)

From Algorithmic Audits to Actual Accountability: Overcoming Practical Roadblocks on the Path to Meaningful Audit Interventions for AI Governance. - Inioluwa Deborah Raji (AIES, 2022)

Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance. - Inioluwa Deborah Raji; Peggy Xu; Colleen Honigsberg; Daniel E. Ho (AIES, 2022)

Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. - Sasha Costanza-Chock; Inioluwa Deborah Raji; Joy Buolamwini (FAccT, 2022)

The Fallacy of AI Functionality. - Inioluwa Deborah Raji; I. Elizabeth Kumar; Aaron Horowitz; Andrew D. Selbst (FAccT, 2022)

Organizational Governance of Emerging Technologies: AI Adoption in Healthcare. - Jee Young Kim; William Boag; Freya Gulamali; Alifia Hasan; Henry David Jeffry Hogg; Mark Lifson; Deirdre K. Mulligan; Manesh Patel; Inioluwa Deborah Raji; Ajai Sehgal; Keo Shaw; Danny Tobey; et al. (FAccT, 2023)

Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. - Inioluwa Deborah Raji; Joy Buolamwini (Commun. ACM, 2023)

REFORMS: Reporting Standards for Machine Learning Based Science. - Sayash Kapoor; Emily Cantrell; Kenny Peng; Thanh Hien Pham; Christopher A. Bail; Odd Erik Gundersen; Jake M. Hofman; Jessica Hullman; Michael A. Lones; Momin M. Malik; Priyanka Nanayakkara; Russell A. Poldrack; et al. (CoRR, 2023)

Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. - Sasha Costanza-Chock; Emma Harvey; Inioluwa Deborah Raji; Martha Czernuszenko; Joy Buolamwini (CoRR, 2023)

"I Searched for a Religious Song in Amharic and Got Sexual Content Instead'': Investigating Online Harm in Low-Resourced Languages on YouTube. - Hellina Hailu Nigatu; Inioluwa Deborah Raji (FAccT, 2024)

The Data Addition Dilemma. - Judy Hanwen Shen; Inioluwa Deborah Raji; Irene Y. Chen (MLHC, 2024)

AI auditing: The Broken Bus on the Road to AI Accountability. - Abeba Birhane; Ryan Steed; Victor Ojewale; Briana Vecchione; Inioluwa Deborah Raji (SaTML, 2024)

Concrete Problems in AI Safety, Revisited. - Inioluwa Deborah Raji; Roel Dobbe (CoRR, 2024)

Evaluating Prediction-based Interventions with Human Decision Makers In Mind. - Inioluwa Deborah Raji; Lydia T. Liu (AISTATS, 2025)

Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling. - Victor Ojewale; Ryan Steed; Briana Vecchione; Abeba Birhane; Inioluwa Deborah Raji (CHI, 2025)

From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms. - Jessica Dai; Paula Gradu; Inioluwa Deborah Raji; Benjamin Recht (CoRR, 2025)

Toward an Evaluation Science for Generative AI Systems. - Laura Weidinger; Inioluwa Deborah Raji; Hanna M. Wallach; Margaret Mitchell; Angelina Wang; Olawale Salaudeen; Rishi Bommasani; Deep Ganguli; Sanmi Koyejo; William Isaac 001 (CoRR, 2025)

Medical Large Language Model Benchmarks Should Prioritize Construct Validity. - Ahmed Alaa; Thomas Hartvigsen; Niloufar Golchini; Shiladitya Dutta; Frances Dean; Inioluwa Deborah Raji; Travis Zack (CoRR, 2025)

The California Report on Frontier AI Policy. - Rishi Bommasani; Scott R. Singer; Ruth E. Appel; Sarah H. Cen; A. Feder Cooper; Elena Cryst; Lindsey A. Gailmard; Ian Klaus; Meredith M. Lee; Inioluwa Deborah Raji; Anka Reuel; Drew Spence; et al. (CoRR, 2025)

Aggregated Individual Reporting for Post-Deployment Evaluation. - Jessica Dai; Inioluwa Deborah Raji; Benjamin Recht; Irene Y. Chen (CoRR, 2025)

Multi-lingual Functional Evaluation for Large Language Models. - Victor Ojewale; Inioluwa Deborah Raji; Suresh Venkatasubramanian (CoRR, 2025)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)