On the Moral Justification of Statistical Parity

Abstract

A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.

arXiv comment11 pages, accepted to ACM FAccT 2021

References in corpus (5)

• Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
• On the (im)possibility of fairness
• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
• Avoiding Disparity Amplification under Different Worldviews

Cited by in corpus (9)

• A Clarification of the Nuances in the Fairness Metrics Landscape
• Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
• Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems
• A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
• Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree
• Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
• On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
• Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
• Building Bridges: Generative Artworks to Explore AI Ethics