Raji, Inioluwa Deborah

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 2,245

Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products

Raji, I. D., & Buolamwini, J. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIFace recognition and analysis

Cited by 651

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns

Ethics and Social Impacts of AIMachine Learning and Data Classification

Cited by 548

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, R. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIFace recognition and analysis

Cited by 299

Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem

Costanza-Chock, S., Raji, I. D., & Buolamwini, J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 277

The Fallacy of AI Functionality

Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 251

Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance

Raji, I. D., Xu, P., Honigsberg, C., & Ho, D. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 209

Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 179

AI auditing: The Broken Bus on the Road to AI Accountability

Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024) · 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 158

AI and the Everything in the Whole Wide World Benchmark

Raji, I. D., Bender, E. M., Paullada, A., Denton, E., & Hanna, A. (2021) · arXiv

Artificial Intelligence ApplicationsPsychiatry, Mental Health, Neuroscience

Cited by 127

Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling

Ojewale, V., Steed, R., Vecchione, B., Birhane, A., & Raji, I. D. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 81

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020) · arXiv

Ethics and Social Impacts of AIFace recognition and analysis

Cited by 69

Development and preliminary testing of health equity across the AI lifecycle (HEAAL): a framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Kim, J. Y., Hasan, A., Kellogg, K. C., Ratliff, W., Murray, S. G., Suresh, H., Valladares, A., Shaw, K., Tobey, D., Vidal, D. E., et al. (2024) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., Mulligan, D., Patel, M., Raji, I. D., Sehgal, A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 55

Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products

Raji, I. D., & Buolamwini, J. (2023) · Communications of the ACM

Ethics and Social Impacts of AIFace recognition and analysis

Cited by 45

It’s Time to Bench the Medical Exam Benchmark

Raji, I. D., Daneshjou, R., & Alsentzer, E. (2025) · NEJM AI

Artificial Intelligence in Healthcare and EducationHealthcare cost, quality, practices

Cited by 42

ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

Raji, I. D., & Yang, J. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 36

On the Legal Compatibility of Fairness Definitions

Xiang, A., & Raji, I. D. (2019) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 34