Confidence Intervals for Testing Disparate Impact in Fair Learning

2018/07/17 by Philippe Besse, Eustasio del Barrio, Besse, Philippe +5 · 1 citation
Computer Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1807.06362

openalex publication_date 2018/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence intervals and not a single value.

Citations

• Fairness in Criminal Justice Risk Assessments: The State of the Art
• Obtaining fairness using optimal transport theory

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