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Amanda Coston
Amanda Coston
Microsoft
Verified email at alumni.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Counterfactual risk assessments, evaluation, and fairness
A Coston, A Mishler, EH Kennedy, A Chouldechova
Proceedings of the 2020 conference on fairness, accountability, and …, 2020
1282020
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
1092019
Conditional learning of fair representations
H Zhao, A Coston, T Adel, GJ Gordon
arXiv preprint arXiv:1910.07162, 2019
1082019
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
782021
Characterizing Fairness Over the Set of Good Models Under Selective Labels
A Coston, A Rambachan, A Chouldechova
International Conference on Machine Learning 139, 2021
732021
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
282023
Counterfactual predictions under runtime confounding
A Coston, E Kennedy, A Chouldechova
Advances in neural information processing systems 33, 4150-4162, 2020
232020
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
212023
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
132023
Counterfactual risk assessments under unmeasured confounding
A Rambachan, A Coston, E Kennedy
arXiv preprint arXiv:2212.09844, 2022
122022
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
62020
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
52023
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
arXiv preprint arXiv:2405.12458, 2024
22024
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
22022
The role of the geometric mean in case-control studies
A Coston, EH Kennedy
arXiv preprint arXiv:2207.09016, 2022
22022
Characterizing fairness over the set of good models under selective labels.” arXiv pre-print
A Coston, A Rambachan, A Chouldechova
22021
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
12024
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
12023
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
A Rambachan, A Coston, E Kennedy
arXiv preprint arXiv:2212.09844, 2022
12022
Predictive Performance Comparison of Decision Policies Under Confounding
L Guerdan, A Coston, K Holstein, ZS Wu
arXiv preprint arXiv:2404.00848, 2024
2024
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