| Fairness without demographics through adversarially reweighted learning P Lahoti, A Beutel, J Chen, K Lee, F Prost, N Thain, X Wang, E Chi Advances in neural information processing systems 33, 728-740, 2020 | 167 | 2020 |
| Debiasing embeddings for reduced gender bias in text classification F Prost, N Thain, T Bolukbasi First Workshop on Gender Bias in Natural Language Processing ACL 2019, 2019 | 51 | 2019 |
| Measuring Recommender System Effects with Simulated Users S Yao, Y Halpern, N Thain, X Wang, K Lee, F Prost, AB H. Chi, Jilin Chen 2nd Workshop on Fairness, Accountability, Transparency, Ethics and Society …, 2020 | 26 | 2020 |
| Practical compositional fairness: Understanding fairness in multi-component recommender systems X Wang, N Thain, A Sinha, F Prost, EH Chi, J Chen, A Beutel Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 19* | 2021 |
| Toward a better trade-off between performance and fairness with kernel-based distribution matching F Prost, H Qian, Q Chen, EH Chi, J Chen, A Beutel NeurIPS 2019 Workshop on Machine Learning with Guarantees, 2019 | 17* | 2019 |
| Understanding and improving fairness-accuracy trade-offs in multi-task learning Y Wang, X Wang, A Beutel, F Prost, J Chen, EH Chi Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 12 | 2021 |
| Measuring model fairness under noisy covariates: A theoretical perspective F Prost, P Awasthi, N Blumm, A Kumthekar, T Potter, L Wei, X Wang, ... Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 873-883, 2021 | 5 | 2021 |
| Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations F Prost, B Packer, J Chen, L Wei, P Kremp, N Blumm, S Wang, T Doshi, ... arXiv preprint arXiv:2210.07755, 2022 | 1 | 2022 |
| Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective AA Kumthekar, A Beutel, EH Chi, F Prost, J Chen, L Wei, N Blumm, ... | | 2021 |
