| Gender shades: Intersectional accuracy disparities in commercial gender classification J Buolamwini, T Gebru Conference on fairness, accountability and transparency, 77-91, 2018 | 6376 | 2018 |
| On the dangers of stochastic parrots: Can language models be too big?🦜 EM Bender, T Gebru, A McMillan-Major, S Shmitchell Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021 | 4560 | 2021 |
| Datasheets for datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, HD Iii, ... Communications of the ACM 64 (12), 86-92, 2021 | 2307 | 2021 |
| 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 | 1999 | 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 | 778 | 2020 |
| Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States T Gebru, J Krause, Y Wang, D Chen, J Deng, EL Aiden, L Fei-Fei Proceedings of the National Academy of Sciences 114 (50), 13108-13113, 2017 | 559 | 2017 |
| Lessons from archives: Strategies for collecting sociocultural data in machine learning ES Jo, T Gebru Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 372 | 2020 |
| 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 AAAI/ACM Conference on AI, Ethics, and Society, 145-151, 2020 | 370 | 2020 |
| Race and gender T Gebru The Oxford handbook of ethics of AI, 251-269, 2020 | 195 | 2020 |
| Fine-grained recognition in the wild: A multi-task domain adaptation approach T Gebru, J Hoffman, L Fei-Fei Proceedings of the IEEE international conference on computer vision, 1349-1358, 2017 | 187 | 2017 |
| Learning features and parts for fine-grained recognition J Krause, T Gebru, J Deng, LJ Li, L Fei-Fei 2014 22nd International conference on pattern recognition, 26-33, 2014 | 153 | 2014 |
| Fine-grained car detection for visual census estimation T Gebru, J Krause, Y Wang, D Chen, J Deng, L Fei-Fei Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 114 | 2017 |
| Diversity and inclusion metrics in subset selection M Mitchell, D Baker, N Moorosi, E Denton, B Hutchinson, A Hanna, ... Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 117-123, 2020 | 112 | 2020 |
| AI Art and its Impact on Artists HH Jiang, L Brown, J Cheng, M Khan, A Gupta, D Workman, A Hanna, ... Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 363-374, 2023 | 108 | 2023 |
| iCassava 2019 fine-grained visual categorization challenge E Mwebaze, T Gebru, A Frome, S Nsumba, J Tusubira arXiv preprint arXiv:1908.02900, 2019 | 78 | 2019 |
| Detecting bias with generative counterfactual face attribute augmentation E Denton, B Hutchinson, M Mitchell, T Gebru, A Zaldivar arXiv preprint arXiv:1906.06439 2 (5), 7, 2019 | 74 | 2019 |
| Proceedings of the 1st Conference on Fairness, Accountability and Transparency J Buolamwini, T Gebru, SA Friedler, C Wilson Proceedings of Machine Learning Research 81, 77-91, 2018 | 57 | 2018 |
| FAccT'21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency EM Bender, T Gebru, A McMillan-Major, S Shmitchell Association for Computing Machinery 2021, 610-623, 2021 | 56 | 2021 |
| Oxford handbook on AI ethics book chapter on race and gender T Gebru arXiv preprint arXiv:1908.06165, 2019 | 53 | 2019 |
| Image counterfactual sensitivity analysis for detecting unintended bias E Denton, B Hutchinson, M Mitchell, T Gebru, A Zaldivar arXiv preprint arXiv:1906.06439, 2019 | 44 | 2019 |
Margaret MitchellResearch Scientist, Hugging Face在 huggingface.co 的电子邮件经过验证
