Data, AI and Society - The Moral Legibility of Narrative and Case Comparison
Channel: ANU TV
Date: 2020-08-20

In this public seminar, the fifth in the HMI Data, AI and Society series, Assistant Professor Barbara Kiviat in the Department of Sociology at Stanford University, evaluates how algorithmic predictions allocate benefits and burdens, on-lookers often turn to storytelling, causal theorizing, and other narrative ways of knowing. 

Kiviat argues that one reason narrative factors so prominently in debates about algorithmic fairness is that stories provide information that algorithms—rooted in the competing logic of case comparison—do not. Whether we understand people as actors in unfolding series of events or as cases with particular attributes determines which aspects of individuals and situations are legible, and, as a result, the types of moral judgments we can make.