| Counterfactual risk assessments, evaluation, and fairness A Coston, A Mishler, EH Kennedy, A Chouldechova Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 125 | 2020 |
| Fair transfer learning with missing protected attributes A Coston, KN Ramamurthy, D Wei, KR Varshney, S Speakman, ... Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 91-98, 2019 | 107 | 2019 |
| Conditional learning of fair representations H Zhao, A Coston, T Adel, GJ Gordon arXiv preprint arXiv:1910.07162, 2019 | 101 | 2019 |
| Leveraging administrative data for bias audits: Assessing disparate coverage with mobility data for COVID-19 policy A Coston, N Guha, D Ouyang, L Lu, A Chouldechova, DE Ho Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 73 | 2021 |
| Characterizing Fairness Over the Set of Good Models Under Selective Labels A Coston, A Rambachan, A Chouldechova International Conference on Machine Learning 139, 2021 | 65 | 2021 |
| A validity perspective on evaluating the justified use of data-driven decision-making algorithms A Coston, A Kawakami, H Zhu, K Holstein, H Heidari 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 690-704, 2023 | 27 | 2023 |
| Counterfactual predictions under runtime confounding A Coston, E Kennedy, A Chouldechova Advances in neural information processing systems 33, 4150-4162, 2020 | 22 | 2020 |
| Ground (less) truth: A causal framework for proxy labels in human-algorithm decision-making L Guerdan, A Coston, ZS Wu, K Holstein Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 17 | 2023 |
| Counterfactual risk assessments under unmeasured confounding A Rambachan, A Coston, E Kennedy arXiv preprint arXiv:2212.09844, 2022 | 11 | 2022 |
| Counterfactual prediction under outcome measurement error L Guerdan, A Coston, K Holstein, ZS Wu Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 9 | 2023 |
| Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate L Li, R Zuo, A Coston, JC Weiss, GH Chen International Conference on Artificial Intelligence in Medicine, 371-381, 2020 | 6 | 2020 |
| Examining risks of racial biases in NLP tools for child protective services A Field, A Coston, N Gandhi, A Chouldechova, E Putnam-Hornstein, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 3 | 2023 |
| Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use A Kawakami, A Coston, H Heidari, K Holstein, H Zhu | 2 | 2023 |
| Enhancing fairness in transfer learning for machine learning models with missing protected attributes in source or target domains KN Ramamurthy, A Coston, D Wei, KR Varshney, S Speakman, ... US Patent 11,443,236, 2022 | 2 | 2022 |
| The role of the geometric mean in case-control studies A Coston, EH Kennedy arXiv preprint arXiv:2207.09016, 2022 | 2 | 2022 |
| Characterizing fairness over the set of good models under selective labels.” arXiv pre-print A Coston, A Rambachan, A Chouldechova | 2 | 2021 |
| Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design A Kawakami, A Coston, H Zhu, H Heidari, K Holstein arXiv preprint arXiv:2303.14602, 2023 | 1 | 2023 |
| Predictive Performance Comparison of Decision Policies Under Confounding L Guerdan, A Coston, K Holstein, ZS Wu arXiv preprint arXiv:2404.00848, 2024 | | 2024 |
| The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals A Kawakami, A Coston, H Zhu, H Heidari, K Holstein arXiv preprint arXiv:2402.18774, 2024 | | 2024 |
| Policy Comparison Under Unmeasured Confounding L Guerdan, A Coston, S Wu, K Holstein NeurIPS 2023 Workshop on Regulatable ML, 2023 | | 2023 |
Alexandra ChouldechovaAssociate Professor of Statistics & Public Policy @ Heinz College, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
