Work

Lazy Data Practices Harm Fairness Research

Year: 2024

Type: conference-paper

Abstract: Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications.

DOI: 10.1145/3630106.3658931

Authors: Jan Simson, Alessandro Fabris, Christoph Kern

Institutions: Munich Center for Machine Learning, Ludwig-Maximilians-Universität München, Max Planck Institute for Security and Privacy

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold