ABOUT
Hi there,
Thanks for stopping by! My name is Madison Coots and I am a third year Ph.D. student in Public Policy at the Harvard Kennedy School of Government and a Stone Ph.D. Scholar in Inequality and Wealth Concentration. I am also a researcher at the Computational Policy Lab. My research interests lie in algorithmic fairness and computational approaches to public policy and decision-making, with applications in healthcare, lending, and criminal justice reform.
Outside of academics, I am a senior data scientist at STR. Previously, I've worked as a data scientist in academia and in the public sector. I also have experience working in the energy and utilities sector as a senior data scientist with Aerospace Technical Services. Before beginning my Ph.D., I studied computer science (M.S.), management science and engineering (B.S.), and English literature (minor) at Stanford.
PROJECTS
Some things I've worked on
Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates
M. Coots, K. Linn, S.Goel, A. Navathe, and R. Parikh
We develop a taxonomy of concerns over the fairness of healthcare algorithms and critically examine seven prominent and controversial healthcare algorithms. We show that popular approaches that aim to improve the fairness of healthcare algorithms can in fact worsen outcomes for individuals and, in turn, offer an alternative, consequentialist framework for algorithm design that mitigates these harms.
Annual Review of Public Health (forthcoming).
Reevaluating the Role of Race and Ethnicity in Diabetes Screening
M. Coots, S. Saghafian, D. Kent, and S.Goel
We show that incorporating race and ethnicity into diabetes risk predictions substantially improves statistical accuracy, confirming past analyses, but argue that these statistical benefits lead to surprisingly modest gains in clinical utility.
Working paper.
Automated Court Date Reminders Reduce Warrants for Arrest: Evidence from a Text Messaging Experiment
A. Chohlas-Wood, M. Coots, J. Nudell, J. Nyarko, E. Brunskill, T. Rogers, and S.Goel
Results from a large field experiment in partnership with the Santa Clara County Public Defender Office that show that automated text message reminders reduced the number of warrants for arrest issued for missing court by over 20%.
Working paper.
Designing Equitable Algorithms
A. Chohlas-Wood, M. Coots, J. Nyarko, and S. Goel
A discussion about the apparent conflicts between formal algorithmic fairness constraints and equitable outcomes, and approaches for practitioners to use in designing more equitable algorithms.
Nature Computational Science (2023).
Learning to be Fair: A Consequentialist Approach to Equitable Decision Making
A. Chohlas-Wood, M. Coots, H. Zhu, E. Brunskill, and S. Goel
A new theoretical framework for how to efficiently allocate limited resources to people in an equitable way.
Management Science (forthcoming).
