Bridging Research Gaps Between Academic Research and Legal Investigations of Algorithmic Discrimination

TL;DR

This paper analyzes legal cases involving algorithmic discrimination to identify research gaps and practical challenges, proposing targeted research opportunities to improve algorithmic fairness in legal contexts.

Contribution

It bridges the gap between academic fairness research and legal investigations by identifying five key practical challenges and offering specific recommendations for aligned research.

Findings

Identified five key research gaps in algorithmic bias.

Analyzed 15 civil enforcement actions to uncover practical challenges.

Provided recommendations for developing tools to support legal cases.

Abstract

As algorithms increasingly take on critical roles in high-stakes areas such as credit scoring, housing, and employment, civil enforcement actions have emerged as a powerful tool for countering potential discrimination. These legal actions increasingly draw on algorithmic fairness research to inform questions such as how to define and detect algorithmic discrimination. However, current algorithmic fairness research, while theoretically rigorous, often fails to address the practical needs of legal investigations. We identify and analyze 15 civil enforcement actions in the United States including regulatory enforcement, class action litigation, and individual lawsuits to identify practical challenges in algorithmic discrimination cases that machine learning research can help address. Our analysis reveals five key research gaps within existing algorithmic bias research, presenting practical opportunities for more aligned research: 1) finding an equally accurate and less discriminatory algorithm, 2) cascading algorithmic bias, 3) quantifying disparate impact, 4) navigating information barriers, and 5) handling missing protected group information. We provide specific recommendations for developing tools and methodologies that can strengthen legal action against unfair algorithms.