Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. - Maria De-Arteaga; Amanda Coston; William Herlands (CoRR, 2018)

Risk Assessments and Fairness Under Missingness and Confounding. - Amanda Coston (AIES, 2019)

Fair Transfer Learning with Missing Protected Attributes. - Amanda Coston; Karthikeyan Natesan Ramamurthy; Dennis Wei; Kush R. Varshney; Skyler Speakman; Zairah Mustahsan; Supriyo Chakraborty (AIES, 2019)

Counterfactual Risk Assessments, Evaluation, and Fairness. - Amanda Coston; Alexandra Chouldechova; Edward H. Kennedy (CoRR, 2019)

Counterfactual risk assessments, evaluation, and fairness. - Amanda Coston; Alan Mishler; Edward H. Kennedy; Alexandra Chouldechova (FAT*, 2020)

Conditional Learning of Fair Representations. - Han Zhao 002; Amanda Coston; Tameem Adel; Geoffrey J. Gordon (ICLR, 2020)

Counterfactual Predictions under Runtime Confounding. - Amanda Coston; Edward H. Kennedy; Alexandra Chouldechova (NeurIPS, 2020)

Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D. - Maria De-Arteaga; Tejumade Afonja; Amanda Coston (CoRR, 2020)

Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. - Amanda Coston; Neel Guha; Derek Ouyang; Lisa Lu; Alexandra Chouldechova; Daniel E. Ho (FAccT, 2021)

Characterizing Fairness Over the Set of Good Models Under Selective Labels. - Amanda Coston; Ashesh Rambachan; Alexandra Chouldechova (ICML, 2021)

Counterfactual Risk Assessments under Unmeasured Confounding. - Ashesh Rambachan; Amanda Coston; Edward H. Kennedy (CoRR, 2022)

Examining risks of racial biases in NLP tools for child protective services. - Anjalie Field; Amanda Coston; Nupoor Gandhi; Alexandra Chouldechova; Emily Putnam-Hornstein; David Steier; Yulia Tsvetkov (FAccT, 2023)

Counterfactual Prediction Under Outcome Measurement Error. - Luke Guerdan; Amanda Coston; Kenneth Holstein; Zhiwei Steven Wu (FAccT, 2023)

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. - Luke Guerdan; Amanda Coston; Zhiwei Steven Wu; Kenneth Holstein (FAccT, 2023)

A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms. - Amanda Coston; Anna Kawakami; Haiyi Zhu; Ken Holstein; Hoda Heidari (SaTML, 2023)

Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design. - Anna Kawakami; Amanda Coston; Haiyi Zhu; Hoda Heidari; Kenneth Holstein (CoRR, 2023)

The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals. - Anna Kawakami; Amanda Coston; Haiyi Zhu; Hoda Heidari; Kenneth Holstein (CHI, 2024)

Predictive Performance Comparison of Decision Policies Under Confounding. - Luke Guerdan; Amanda Coston; Ken Holstein; Steven Wu 001 (ICML, 2024)

Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate. - George H. Chen; Linhong Li; Ren Zuo; Amanda Coston; Jeremy C. Weiss (Artif. Intell. Medicine, 2024)

Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use. - Anna Kawakami; Amanda Coston; Hoda Heidari; Kenneth Holstein; Haiyi Zhu (Proc. ACM Hum. Comput. Interact., 2024)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)