SCALES: From Fairness Principles to Constrained Decision-Making
- URL: http://arxiv.org/abs/2209.10860v1
- Date: Thu, 22 Sep 2022 08:44:36 GMT
- Title: SCALES: From Fairness Principles to Constrained Decision-Making
- Authors: Sreejith Balakrishnan, Jianxin Bi, Harold Soh
- Abstract summary: We show that well-known fairness principles can be encoded either as a utility component, a non-causal component, or a causal component.
We show that our framework produces fair policies that embody alternative fairness principles in single-step and sequential decision-making scenarios.
- Score: 16.906822244101445
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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