The Flawed Foundations of Fair Machine Learning

TL;DR

This paper critically examines the foundational assumptions of fair machine learning, revealing inherent trade-offs and misconceptions, and offers a proof-of-concept evaluation to better understand fairness constraints.

Contribution

It clarifies the true nature of fairness in machine learning, exposes the trade-off with accuracy, and introduces a proof-of-concept to analyze these relationships.

Findings

Trade-off exists between accuracy and fairness in data with disparities.

Current fairness definitions are limited to group similarity based on sensitive attributes.

A proof-of-concept evaluation demonstrates the relationship between fairness and accuracy.

Abstract

The definition and implementation of fairness in automated decisions has been extensively studied by the research community. Yet, there hides fallacious reasoning, misleading assertions, and questionable practices at the foundations of the current fair machine learning paradigm. Those flaws are the result of a failure to understand that the trade-off between statistically accurate outcomes and group similar outcomes exists as independent, external constraint rather than as a subjective manifestation as has been commonly argued. First, we explain that there is only one conception of fairness present in the fair machine learning literature: group similarity of outcomes based on a sensitive attribute where the similarity benefits an underprivileged group. Second, we show that there is, in fact, a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist, and that the trade-off presents an existential threat to the equitable, fair machine learning approach. Third, we introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes. Finally, suggestions for future work aimed at data scientists, legal scholars, and data ethicists that utilize the conceptual and experimental framework described throughout this article are provided.