Algorithmic Bias: Sources and Potential Solutions

CEDA brings to you a lecture on the topic: Algorithmic Bias: Sources and Potential Solutions

Abstract: Today’s machine learning algorithms are trained on data generated by human decisions. Thus, these algorithms are prone to learn (and amplify) the existing human bias in the data. Moreover, the black-box nature of these algorithms makes it difficult to detect such biases. In this talk, we will try to understand the sources of algorithmic bias and the potential solutions. We will also talk about the ongoing research in this area and the opportunities for future research.

Speaker’s Bio: Manmohan Aseri is an assistant professor of Business Administration at the University of Pittsburgh. His research primarily focuses on the economics of AI. Within this area, he studies algorithmic bias, fair advertising, AI in performance evaluation, bias in job descriptions, and several other topics at the intersection of AI and society.

Date: 29th April 2021
Time: 6:30 PM (IST) , 9:00AM (EST)
Zoom Link: tinyurl.com/AlgorithmicBias