Sentiments and Bias in Automated Decision Making

Jesse Hoey

Abstract: I will discuss how computational models of human (social) affect may be used to help mitigate biases in algorithmic decision making. I consider the more general "shortlist" problem of how to select the set of choices over which a decision maker can ponder. As the choices on a ballot are as important as the votes themselves, the decisions of who to hire, who to insure, or who to admit, are directly dependent to who is considered, who is categorized, or who meets the threshold for admittance. I will frame this problem as one requiring additional non-epistemic (affective) context that normalizes expected values, and propose a computational model for this context based on a social-psychological model of affect in social interactions.