| 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 | 977 | 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 | 354 | 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 | 343 | 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), 100336, 2021 | 208 | 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 | 197 | 2020 |
| AI Now 2019 Report K Crawford, R Dobbe, T Dryer, G Fried, B Green, E Kaziunas, A Kak, ... | 194* | 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 | 72 | 2021 |
| 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 | 41 | 2021 |
| On the legal compatibility of fairness definitions A Xiang, ID Raji arXiv preprint arXiv:1912.00761, 2019 | 40 | 2019 |
| About face: A survey of facial recognition evaluation ID Raji, G Fried arXiv preprint arXiv:2102.00813, 2021 | 38 | 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 | 25 | 2021 |
| About ml: Annotation and benchmarking on understanding and transparency of machine learning lifecycles ID Raji, J Yang arXiv preprint arXiv:1912.06166, 2019 | 24 | 2019 |
| Participatory approaches to machine learning B Kulynych, D Madras, S Milli, ID Raji, A Zhou, R Zemel International Conference on Machine Learning Workshop, 2020 | 22 | 2020 |
| The fallacy of AI functionality ID Raji, IE Kumar, A Horowitz, A Selbst 2022 ACM Conference on Fairness, Accountability, and Transparency, 959-972, 2022 | 13 | 2022 |
| Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem S Costanza-Chock, ID Raji, J Buolamwini 2022 ACM Conference on Fairness, Accountability, and Transparency, 1571-1583, 2022 | 9 | 2022 |
| AI Now Report 2019 K Crawford, R Dobbe, T Dryer, G Fried, B Green, E Kaziunas, A Kak, ... New York: AI Now Institute, 2019 | 9 | 2019 |
| The discomfort of death counts: mourning through the distorted lens of reported COVID-19 death data ID Raji Patterns 1 (4), 100066, 2020 | 7 | 2020 |
| Fake AI F Kaltheuner Meatspace press, 2021 | 5 | 2021 |
| AI now 2019 report C Kate, R Dobbe, T Dryerand, G Fried, B Green, E Kaziunas, A Kak, ... New York: AI Now Institute. ainowinstitute. org/AI_Now_2019_Report. html, 2019 | 5 | 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 | 4 | 2022 |
