Many different things are said to be biased. People, of course, are biased. But so too are groups of people—organizations, news programs, political parties—as well as parts of people, such as an individual's perceptual system or their reasoning capacities. Sometimes inanimate objects, like coins or—most recently—algorithms, are said to be biased. What, if anything, do all of these biases have in common?
This conference aims to bridge the gap between the humanities and the sciences by providing a critical perspective on theories of social bias at the intersection of philosophy, psychology, and computer science. Much of the empirical research on bias concerns study of individualized cases of particular biases rather than the phenomena more generally, and theoretical research on bias is siloed within different traditions and methodologies without much dialogue across them. As technological innovations pick up on and exacerbate human social biases, there is a pressing need for an understanding of bias and its operation in minds, machines and societies.
Louise Antony (UMass Amherst)
Mahzarin Banaji (Harvard)
Michael Brownstein (CUNY)
Jim Chamberlain, Jules Holroyd, Ben Jenkins, and Robin Scaife (University of Sheffield)
Eugene Chua (UC San Diego)
Tina Eliassi-Rad (Northeastern)
Lily Hu (Yale)
Rachel Rudolph, Elay Shech, and Mike Tamir (Auburn and UC Berkeley)
Manon Andre St. Amant (University of Minnesota)
Gabbrielle Johnson (Claremont McKenna College)
Jessie Munton (University of Cambridge)
Nathaniel Braswell (Claremont McKenna College)