| Model cards for model reporting M Mitchell, S Wu, A Zaldivar, P Barnes, L Vasserman, B Hutchinson, ... Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 1713 | 2019 |
| Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing ID Raji, A Smart, RN White, M Mitchell, T Gebru, B Hutchinson, ... Proceedings of the 2020 Conference on Fairness, Accountability and …, 2020 | 675 | 2020 |
| Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products ID Raji, J Buolamwini Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 429-435, 2019 | 549 | 2019 |
| Data and its (dis) contents: A survey of dataset development and use in machine learning research A Paullada, ID Raji, EM Bender, E Denton, A Hanna Patterns 2 (11), 2021 | 482 | 2021 |
| Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing ID Raji, T Gebru, M Mitchell, J Buolamwini, J Lee, E Denton Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society, 145-151, 2020 | 324 | 2020 |
| AI Now 2019 Report K Crawford, R Dobbe, T Dryer, G Fried, B Green, E Kaziunas, A Kak, ... | 282* | 2019 |
| AI and the Everything in the Whole Wide World Benchmark ID Raji, EM Bender, A Paullada, E Denton, A Hanna Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 194 | 2021 |
| The fallacy of AI functionality ID Raji, IE Kumar, A Horowitz, A Selbst Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 132 | 2022 |
| You can't sit with us: Exclusionary pedagogy in ai ethics education ID Raji, MK Scheuerman, R Amironesei Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021 | 100 | 2021 |
| Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem S Costanza-Chock, ID Raji, J Buolamwini Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 91 | 2022 |
| Are we learning yet? a meta review of evaluation failures across machine learning T Liao, R Taori, ID Raji, L Schmidt Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 84 | 2021 |
| About face: A survey of facial recognition evaluation ID Raji, G Fried AAAI 2020 Workshop on AI Evaluation, 2021 | 74 | 2021 |
| On the legal compatibility of fairness definitions A Xiang, ID Raji Workshop on Human-Centric Machine Learning at the 33rd Conference on Neural …, 2019 | 62 | 2019 |
| Outsider oversight: Designing a third party audit ecosystem for ai governance ID Raji, P Xu, C Honigsberg, D Ho Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 557-571, 2022 | 56 | 2022 |
| Participatory approaches to machine learning B Kulynych, D Madras, S Milli, ID Raji, A Zhou, R Zemel International Conference on Machine Learning Workshop 7, 2020 | 37 | 2020 |
| About ml: Annotation and benchmarking on understanding and transparency of machine learning lifecycles ID Raji, J Yang Human-Centric Machine Learning workshop at Neural Information Processing …, 2019 | 35 | 2019 |
| Fake ai F Kaltheuner Meatspace press, 2021 | 28 | 2021 |
| Actionable auditing revisited: Investigating the impact of publicly naming biased performance results of commercial ai products ID Raji, J Buolamwini Communications of the ACM 66 (1), 101-108, 2022 | 19 | 2022 |
| Organizational governance of emerging technologies: AI adoption in healthcare JY Kim, W Boag, F Gulamali, A Hasan, HDJ Hogg, M Lifson, D Mulligan, ... proceedings of the 2023 ACM conference on fairness, accountability, and …, 2023 | 14 | 2023 |
| Ai regulation has its own alignment problem: The technical and institutional feasibility of disclosure, registration, licensing, and auditing N Guha, C Lawrence, LA Gailmard, K Rodolfa, F Surani, R Bommasani, ... George Washington Law Review, Forthcoming, 2023 | 10 | 2023 |
