Fairness in algorithmic decision-making: theory & applications

Presented by:
Nisarg Shah, Assistant Professor

Our weekly seminar series welcomes Schwartz Reisman Faculty Affiliate Nisarg Shah, assistant professor in the Department of Computer Science at the University of Toronto.

Shah’s research lies at the intersection of computer science and economics and focuses on issues of fairness, efficiency, elicitation, and incentives that arise when humans are affected by algorithmic decision-making.

Abstract:

Fairness in algorithmic decision-making has been studied for decades at the intersection of economics and computer science in the context of resource allocation. Its real-world applications include inheritance division, allocation of CPU and RAM in computing environments, and rent-sharing among roommates.

In this talk, I will first discuss our theoretical work on fair division and how it has helped tens of thousands of people fairly divide resources through our not-for-profit website Spliddit.org. Then, I will argue that these economic notions of fairness are also quite applicable to modern algorithmic paradigms such as machine learning. 

This will be a high-level talk that will cover seven papers under two areas:

• Fair division

• Fairness in machine learning