Work

SCALES: From Fairness Principles to Constrained Decision-Making

Year: 2022

Type: conference-paper

Abstract: This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP). With the help of causal language, our framework can place constraints on both the procedure of decision making (procedural fairness) as well as the outcomes resulting from decisions (outcome fairness). Specifically, we show that well-known fairness principles can be encoded either as a utility component, a non-causal component, or a causal component in a SCALES-CMDP. We illustrate SCALES using a set of case studies involving a simulated healthcare scenario and the real-world COMPAS dataset. Experiments demonstrate that our framework produces fair policies that embody alternative fairness principles in single-step and sequential decision-making scenarios.

Source: Proceedings of the AAAI/ACM Conference on AI Ethics and Society

DOI: 10.1145/3514094.3534190

Authors: Sreejith Balakrishnan, Jianxin Bi, Harold Soh

Institution: National University of Singapore

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Peace, justice, and strong institutions

Open Access status: bronze

Funders: National Research Foundation, National Research Foundation Singapore