Amanda Coston 2023 Early Career Fellow
Affiliation Postdoctoral Researcher, Microsoft; Incoming Assistant Professor, UC Berkeley Hard Problem Solved AI’s continually evolving safety and security, robustness, performance, output challenges and other shortcomings that may cause harm or erode public trust of AI systems, especially in safety-critical applications and uses where societal stakes and potential for societal harm are high.

Amanda Coston is a Postdoc at Microsoft Research and an Incoming Assistant Professor of Statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity, reliability, and equity of algorithmic decision support systems and data-driven policy-making. Amanda earned her PhD in Machine Learning and Public Policy at Carnegie Mellon University where she was advised by Alexandra Chouldechova and Edward H. Kennedy. After her PhD, Amanda worked at Microsoft Research on the Machine Learning and Statistics Team as a postdoc researcher. Amanda is a Rising Star in EECS, Machine Learning, and Data Science, a Meta Research PhD Fellow, NSF GRFP Fellow, K & L Gates Presidential Fellow in Ethics and Computational Technologies, and Tata Consultancy Services Presidential Fellow. Her work has been recognized by best paper awards and featured in The Wall Street Journal and VentureBeat.

AI2050 Project

How can we ensure AI does what it is supposed to? Amanda Coston’s AI2050 project focuses on predictive AI — when AI is used to predict outcomes of interest. These predictions can be used to inform decision making, often in high-stakes settings like healthcare or criminal justice. Coston’s project uses techniques from nonparametric statistics, human-computer interaction, and the social sciences to develop tools to assess whether AI predicts the intended quantity and whether it will behave as intended in the real-world. This will advance Hard Problem 2 (Assurance) by providing a rigorous framework to establish the validity of AI.