| 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 | 2203 | 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 | 867 | 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 | 689 | 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 | 626 | 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 | 402 | 2020 |
| AI Now 2019 Report K Crawford, R Dobbe, T Dryer, G Fried, B Green, E Kaziunas, A Kak, ... | 325* | 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 | 290 | 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 | 228 | 2022 |
| 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 | 149 | 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 | 133 | 2021 |
| 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 | 121 | 2021 |
| 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 | 88 | 2022 |
| About face: A survey of facial recognition evaluation ID Raji, G Fried AAAI 2020 Workshop on AI Evaluation, 2021 | 78 | 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 | 68 | 2019 |
| 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 | 44 | 2019 |
| REFORMS: Consensus-based Recommendations for Machine-learning-based Science S Kapoor, EM Cantrell, K Peng, TH Pham, CA Bail, OE Gundersen, ... Science Advances 10 (18), eadk3452, 2024 | 43* | 2024 |
| 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 | 43 | 2020 |
| Fake ai F Kaltheuner Meatspace press, 2021 | 35 | 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 | 34 | 2022 |
| SoK: AI Auditing: The Broken Bus on the Road to AI Accountability A Birhane, R Steed, V Ojewale, B Vecchione, ID Raji 2nd IEEE Conference on Secure and Trustworthy Machine Learning, 2024 | 29* | 2024 |
