Algorithmic bias is now recognized as a significant potential problem in many contexts, and a cottage industry has developed with measures and responses to findings of bias. In this talk, I will first argue that this dominant approach to algorithmic bias is misguided, both philosophically and technically. I will then develop a qualitatively different framework for understanding and assessing algorithmic biases, including an exploration of the ways in which the framework can help us to understand the limits of our ability to discover societal biases using data.
David Danks is L.L. Thurstone Professor of Philosophy & Psychology, and Head of the Department of Philosophy, at Carnegie Mellon University. He is also the Chief Ethicist of CMU’s Block Center for Technology & Society; and co-director of CMU’s Center for Informed Democracy and Social Cybersecurity (IDeaS). His research interests are at the intersection of philosophy, cognitive science, and machine learning, using ideas, methods, and frameworks from each to advance our understanding of complex, interdisciplinary problems.
The LTI Colloquium is generously sponsored by Abridge.
Zoom Participation. See announcement.