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.
Index Terms
- Sentiments and Bias in Automated Decision Making
Recommendations
Decision making process: typology, intelligence, and optimization
Decision making is concerned with evaluating and/or ranking possible alternatives of action. In this paper, we develop a model for the process of decision making. Understanding the decision process can provide insights into how humans make decisions, ...
Sustainable decision making: the role of decision support systems
Sustainable decision making stands for decision making which contributes to the transition to a sustainable society. It raises a number of challenging problems for which existing decision support systems (DSS) may not be equipped. The role of DSS in ...
Consensus-based decision support for multicriteria group decision making
Consensus decision making is complex and challenging in multicriteria group decision making due to the involvement of several decision makers, the presence of multiple, and often conflicting criteria, and the existence of subjectiveness and imprecision ...
Comments