Champion, Heather (2025) On Values in Fairness Optimization with Machine Learning. [Preprint]

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Abstract

Statistical criteria of fairness, though controversial, bring attention to the multiobjective nature of many predictive modelling problems. In this paper, I consider how epistemic and non-epistemic values impact the design of machine learning algorithms that optimize for more than one normative goal. I focus on a major design choice between biased search strategies that directly incorporate priorities for various objectives into an optimization procedure, and unbiased search strategies that do not. I argue that both reliably generate Pareto optimal solutions such that various other values are relevant to making a rational choice between them.

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Item Type:	Preprint
Creators:	Creators Email ORCID Champion, Heather hchampi2@uwo.ca 0000-0001-7304-2592
Keywords:	fairness, machine learning, optimization, values
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Depositing User:	Unnamed user with email hchampi2@uwo.ca
Date Deposited:	30 Jul 2025 12:52
Last Modified:	30 Jul 2025 12:52
Item ID:	26052
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Date:	2025
URI:	https://philsci-archive.pitt.edu/id/eprint/26052

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