Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 539 | 2023 |
Towards accountability for machine learning datasets: Practices from software engineering and infrastructure B Hutchinson, A Smart, A Hanna, E Denton, C Greer, O Kjartansson, ... Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 276 | 2021 |
Evaluation gaps in machine learning practice B Hutchinson, N Rostamzadeh, C Greer, K Heller, V Prabhakaran Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 37 | 2022 |
Measuring model biases in the absence of ground truth O Aka, K Burke, A Bauerle, C Greer, M Mitchell Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 327-335, 2021 | 31 | 2021 |
Introducing the model card toolkit for easier model transparency reporting H Fang, H Miao, K Shukla, D Nanas, C Xu, C Greer, N Polyzotis, T Doshi, ... Google AI Blog, 2020 | 15 | 2020 |
Thinking beyond distributions in testing machine learned models N Rostamzadeh, B Hutchinson, C Greer, V Prabhakaran arXiv preprint arXiv:2112.03057, 2021 | 5 | 2021 |
Visual Identification of Problematic Bias in Large Label Spaces A Bäuerle, AG Turker, K Burke, O Aka, T Ropinski, C Greer, ... arXiv preprint arXiv:2201.06386, 2022 | 3 | 2022 |
Towards accountability for machine learning datasets A Hanna, A Smart, B Hutchinson, C Greer, E Denton, M Mitchell, ... Proceedings of the Conference on Fairness, Accountability, and Transparency …, 2021 | 3 | 2021 |
Fairness Indicators Demo: Scalable Infrastructure for Fair ML Systems C Xu, C Greer, MN Joshi, T Doshi | 2 | 2020 |
Rethinking Testing of Machine Learned Models N Rostamzadeh, B Hutchinson, V Prabhakaran, C Greer | | 2022 |
Critical Evaluation Gaps in Machine Learning Practice B Hutchinson, K Heller, N Rostamzadeh, V Prabhakaran | | 2022 |