Fairness and Privacy in Practice:

Solving for Big Data’s Big Ethics Problems

January @ GSAS Mini Course | Jan 21-24, 2020

A growing number of experiences in human life are driven by machine predictions, impacting everything from the news that you see online to whether or not you’re selected for secondary screening at the airport. But many applications of the underlying algorithmic tools are imperfect: publicly available Census data can be used to reidentify 17% of U.S. residents’ private responses, and until recently, Facebook’s marketing platform enabled advertisers to target housing ads based on a user’s inferred race. In a sort of ‘Wild West’ of applied big data analytics, unregulated algorithms that make decisions at scale can also violate norms of fairness and privacy at scale and in ways their developers don’t always understand.

Course Overview

In this mini-course, we will bridge the gaps between industry and research, policy and technology. After an introduction to fundamental concepts in law, ethics, and CS, we will analyze algorithmic decision-making through the interdisciplinary lens of five real-world case studies. Each interactive case will surface the unique perspectives of students and professionals from numerous fields, creating an interdisciplinary dialogue on strategies to implement fair and private algorithmic decision-making. Our goal: synthesize new ethical and computational approaches that could provide greater fairness and privacy guarantees.

Note: Enrollment is open to the public but capped at 20 to promote active engagement and discussion in this seminar-style mini-course.