Lazy Data Practices Harm Fairness Research

arXiv:2404.17293v1 Announce Type: new
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 …

abstract advancement analysis arxiv cs.cy cs.lg data datasets fair fairness harm highlighting improvement lazy machine machine learning practice practices recommendations reflections research responsible stat.ap stat.ml studies type work