A January@GSAS mini-course on ethical and computational approaches to more fair and private decision-making with big data.
This mini-course aims to engage participants from across careers and academic fields in an interdisciplinary dialogue on strategies to implement fair and private algorithmic decision-making. After an introduction to fundamental concepts in the policy and technology spheres, students will analyze fair and private algorithmic decision-making through the lens of five real-world case studies, and synthesize new approaches that could provide greater fairness and privacy guarantees.
Meeting Time and Location
Tue Jan 21 – Fri Jan 24, 2020
2:00-4:30 PM | Harvard Campus
Open To The Public, No Prerequisites
Matthew Finney applies data to advise and implement strategic decisions that enhance human outcomes. His current research focuses on statistical methods and computation at scale, as well as the ethical challenges these tools present.
Previously, he led advanced analytics consulting teams that scaled human decision-making with Artificial Intelligence and Machine Learning, enabling optimized resource allocation, targeted interventions, and more efficient operations. In this role, he advised Fortune Global 100 organizations on how to introduce data to executive and operational decision-making processes while working within regulatory and ethical bounds. He is currently pursuing his M.S. in Data Science at Harvard.
The course will kick off with an introduction to legal, ethical, and technical concepts that underpin discussions of fairness and privacy in the information era.
The meat of the course will be delivered using the case method, with cases based on real-world algorithmic decision-making practices in five distinct domains, each selected to present a different set of ethical, technical, and regulatory imperatives. Each case is designed to promote interdisciplinary analysis of the interplay between business objectives, ethical considerations, and technical applications, with the goal of developing alternative technical and ethical approaches that could lead to more fair or more private decision-making.
Each case method session will follow the following format:
This active course format will encourage participants to bring their own experiences and perspectives to a discussion of where we are and where we ought to be with respect to fair and private algorithmic decision-making. The case method has been shown to “reinforce the value of coming prepared, thinking independently, listening carefully, and working as a team”, which will enhance the mini-course experience for students and instructor alike.
This mini-course draws heavily on the research of Professors Cynthia Dwork and Martha Minow at Harvard University, whose course Fairness and Privacy: Perspectives of Law and Probability was the inspiration for this weeklong seminar applying academic research to real-world problem sets.