Date & Time:
February 23, 2021 1:00 pm – 2:00 pm
Location:
Live Stream
02/23/2021 01:00 PM 02/23/2021 02:00 PM America/Chicago Manish Raghavan (Cornell) – The Societal Impacts of Algorithmic Decision-Making Live Stream

The Societal Impacts of Algorithmic Decision-Making

Watch via live stream

Algorithms and AI systems are used to make decisions about people in a variety of contexts, including lending, hiring, and healthcare. Algorithms provide the potential to make consistent and scalable decisions, but they also introduce a number of new challenges. Researchers and domain experts have raised concerns over issues including fairness, accountability, and transparency, which has led to a fast-growing field of research in these subjects.

In this talk, I'll discuss my efforts to develop principles for the responsible development and deployment of algorithmic decision-making systems. I'll provide an overview of the types of societal impacts and values implicated when algorithms are used to make consequential decisions. Situating these issues in contexts like criminal justice and employment, I'll explore how technical tools can help us better understand normative goals like fairness, counteract human biases in decision-making, and reason about the legal and policy implications of AI systems.

Host: Blase Ur

Manish Raghavan

PhD Candidate, Cornell University

Manish is a final-year PhD candidate in the Computer Science department at Cornell University, where he is advised by Jon Kleinberg. He is supported by an NSF GRFP award and a Microsoft Research PhD Fellowship. He received his B.S. in Electrical Engineering and Computer Science from UC Berkeley in May 2016. His primary interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. He is a member of Cornell's Artificial Intelligence, Policy, and Practice initiative and the Mechanism Design for Social Good working group on Bias, Discrimination, and Fairness.

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