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      Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making

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          Abstract

          In the dominant paradigm for designing equitable machine learning systems, one works to ensure that model predictions satisfy various fairness criteria, such as parity in error rates across race, gender, and other legally protected traits. That approach, however, typically divorces predictions from the downstream outcomes they ultimately affect, and, as a result, can induce unexpected harms. Here we present an alternative framework for fairness that directly anticipates the consequences of actions. Stakeholders first specify preferences over the possible outcomes of an algorithmically informed decision-making process. For example, lenders may prefer extending credit to those most likely to repay a loan, while also preferring similar lending rates across neighborhoods. One then searches the space of decision policies to maximize the specified utility. We develop and describe a method for efficiently learning these optimal policies from data for a large family of expressive utility functions, facilitating a more holistic approach to equitable decision-making.

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          Author and article information

          Journal
          17 September 2021
          Article
          2109.08792
          388cff4d-b50a-4aa8-8d7a-47acc18707dc

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          cs.LG cs.CY

          Applied computer science,Artificial intelligence
          Applied computer science, Artificial intelligence

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