Amanda Coston is a PhD student in Machine Learning and Public Policy at Carnegie Mellon University (CMU). Her research considers the impact of algorithmic risk assessments in settings such as child welfare screening, criminal justice, and loan approvals. She is particularly interested in how techniques from causal inference and transfer learning can resolve current limitations of these systems. She is advised by Alexandra Chouldechova and Edward H. Kennedy.

Featured Research

A. Coston, N. Guha, L. Lu, D. Ouyang, A. Chouldechova, and D. Ho. "Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy." ACM Conference on Fairness, Accountability, and Transparency, 2021. [Paper] [ArXiv] [Talk]

A. Coston, A. Rambachan, and A. Chouldechova. "Characterizing Fairness over the Set of Good Models Under Selective Labels." International Conference on Machine Learning, 2021 (to appear). [ArXiv]

A. Coston, E. H. Kennedy, and A. Chouldechova. "Counterfactual Predictions under Runtime Confounding." Neural Information Processing Systems, 2020. [Paper] [ArXiv] [Blog]

A. Coston, A. Mishler, E. H. Kennedy, and A. Chouldechova. "Counterfactual Risk Assessments, Evaluation, and Fairness." ACM FAT* 2020. [Paper] [ArXiv] [Talk]

A. Coston, K. N. Ramamurthy, D. Wei, K. R. Varshney, S. Speakman, Z. Mustahsan, S. Chakraborty. "Fair Transfer Learning with Missing Protected Attributes," AAAI/ACM Conference on Artificial Intellligence, Ethics, and Society (AIES), 2019. [Paper]