Algorithmic Bias: Sources and Responses
Featuring a keynote by Big Data Scientist and New York Times Bestselling Author
"Algorithms: for whom do they fail?"
Plus panels featuring:
- Shaun Barry, Global Leader for Government, Healthcare, and Utilities at SAS
- Solon Barocas, Principal Research at Microsoft Research, Assistant Professor in the Department of Information Science at Cornell University, Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University
- Kevin Bowyer, Schubmehl-Prein Professor of Computer Science and Engineering at the University of Notre Dame
- Genevieve Fried, TechCongress Fellow
- Sara Jordan, Policy Counsel at Future of Privacy Forum
- Kirsten Martin, William P. and Hazel B. White Center Professor of Technology Ethics, IT, Analytics, and Operations at the University of Notre Dame
- Ron Metoyer, Associate Professor of Computer Science and Engineering at the University of Notre Dame (moderator)
- Scott Nestler, Academic Director, MS in Business Analytics at Mendoza College of Business, University of Notre Dame (moderator)
- Mutale Nkonde, CEO of AI For the People
- Francesca Rossi, IBM fellow and AI Ethics Global Leader
- Kate Vredenburgh, Postdoctoral Fellow at Stanford University's Institute for Human-Centered Artificial Intelligence and McCoy Family Center for Ethics and Society
- Michael Zimmer, Associate Professor, Department of Computer Science, Marquette University
This event will explore questions such as:
- What do we mean by "bias" in the context of algorithms?
- What are the sources of that bias?
- In what contexts does bias manifest, and what are the associated harms?
- What technological, institutional, and policy responses best address the various sources of bias?
12 p.m. - Keynote
1 p.m. - Panel 1 - What do we mean by “bias” when we talk about “algorithmic bias”? What are the sources of that bias? In what contexts does it manifest, and what problems does it cause?
2 p.m. - Panel 2 - What ethical obligations do developers/institutions have in accounting for bias in algorithmic decisionmaking? What technical, institutional, and legal responses are best suited to dealing with the problem?
Originally published at techethics.nd.edu.