A Clarification of the Nuances in the Fairness Metrics Landscape

2021/06/30 by Alessandro Castelnovo, Riccardo Crupi, Greta Greco +4 · 2 citations
Computer Science · Mathematics · Social Sciences · #Adversarial Robustness in Machine Learning #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #cs.CY #cs.LG #stat.ML

paper · pdf · doi:10.1038/s41598-022-07939-1

published as Sci Rep 12, 4209 (2022) · 26 pages, 7 figures, 2 tables, title updated: previous title was "The Zoo of Fairness metrics in Machine Learning", authors updated

openalex publication_date 2022/03/10 · arxiv created 2022/03/11 · arxiv updated 2022/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing with Artificial Intelligence. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. The precise differences, implications and "orthogonality" between these notions have not yet been fully analyzed in the literature. In this work, we try to make some order out of this zoo of definitions.

Citations

• The Variational Fair Autoencoder
• Bias in computer systems
• Certifying and Removing Disparate Impact
• Fairness through awareness
• Equality of Opportunity in Supervised Learning
• Fairness in Criminal Justice Risk Assessments: The State of the Art
• A Convex Framework for Fair Regression
• Counterfactual Fairness
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
• The Frontiers of Fairness in Machine Learning
• AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations
• Review of Causal Discovery Methods Based on Graphical Models
• A Survey Of Methods For Explaining Black Box Models
• Transport-based Counterfactual Models
• Inherent Trade-Offs in the Fair Determination of Risk Scores

Cited by

• Human, Algorithm, or Both? Gender Bias in Human-Augmented Recruiting
• Fairness (machine learning) [wikipedia]

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• Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
• Survey on Fairness Notions and Related Tensions
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• The Authority of "Fair" in Machine Learning
• Statistical Equity: A Fairness Classification Objective