Amanda Coston

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 31

Top Topics

Measurement Errors

Risk Assessment

Top Venues

Publications

Anna Kawakami, Amanda Coston, Hoda Heidari, Kenneth Holstein, Haiyi Zhu
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use. CoRR (2024)
Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth Holstein
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals. CoRR (2024)
George H. Chen, Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss
Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate. Artif. Intell. Medicine 154 (2024)
Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth Holstein
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals. CHI (2024)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR (2024)
Anjalie Field, Amanda Coston, Nupoor Gandhi, Alexandra Chouldechova, Emily Putnam-Hornstein, David Steier, Yulia Tsvetkov
Examining risks of racial biases in NLP tools for child protective services. CoRR (2023)
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. CoRR (2023)
Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth Holstein
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design. CoRR (2023)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Counterfactual Prediction Under Outcome Measurement Error. FAccT (2023)
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. FAccT (2023)
Anjalie Field, Amanda Coston, Nupoor Gandhi, Alexandra Chouldechova, Emily Putnam-Hornstein, David Steier, Yulia Tsvetkov
Examining risks of racial biases in NLP tools for child protective services. FAccT (2023)
Amanda Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, Hoda Heidari
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. SaTML (2023)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Counterfactual Prediction Under Outcome Measurement Error. CoRR (2023)
Amanda Coston, Anna Kawakami, Haiyi Zhu, Ken Holstein, Hoda Heidari
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. CoRR (2022)
Ashesh Rambachan, Amanda Coston, Edward H. Kennedy
Counterfactual Risk Assessments under Unmeasured Confounding. CoRR (2022)
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Characterizing Fairness Over the Set of Good Models Under Selective Labels. CoRR (2021)
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Characterizing Fairness Over the Set of Good Models Under Selective Labels. ICML (2021)
Amanda Coston, Neel Guha, Derek Ouyang, Lisa Lu, Alexandra Chouldechova, Daniel E. Ho
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. FAccT (2021)
Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
Counterfactual Predictions under Runtime Confounding. NeurIPS (2020)
Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss, George H. Chen
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate. AIME (2020)
Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss, George H. Chen
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate. CoRR (2020)
Amanda Coston, Neel Guha, Derek Ouyang, Lisa Lu, Alexandra Chouldechova, Daniel E. Ho
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. CoRR (2020)
Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
Counterfactual Predictions under Runtime Confounding. CoRR (2020)
Amanda Coston, Alan Mishler, Edward H. Kennedy, Alexandra Chouldechova
Counterfactual risk assessments, evaluation, and fairness. FAT* (2020)
Maria De-Arteaga, Tejumade Afonja, Amanda Coston
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D. CoRR (2020)
Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon
Conditional Learning of Fair Representations. ICLR (2020)
Amanda Coston, Alexandra Chouldechova, Edward H. Kennedy
Counterfactual Risk Assessments, Evaluation, and Fairness. CoRR (2019)
Amanda Coston
Risk Assessments and Fairness Under Missingness and Confounding. AIES (2019)
Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon
Conditional Learning of Fair Representations. CoRR (2019)
Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty
Fair Transfer Learning with Missing Protected Attributes. AIES (2019)
Maria De-Arteaga, Amanda Coston, William Herlands
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. CoRR (2018)