The Authority of "Fair" in Machine Learning

2017/06/29 by Michael Skirpan, Skirpan, Michael, Micha Gorelick +1 · 1 citation
Computer Science · Social Sciences · #Blockchain Technology Applications and Security #Computers and Society (cs.CY) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Law, AI, and Intellectual Property

paper · pdf · doi:10.48550/arxiv.1706.09976

openalex publication_date 2017/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.

Citations

• Scalable Bayesian Rule Lists
• The Case for Temporal Transparency: Detecting Policy Change Events in\n Black-Box Decision Making Systems
• A statistical framework for fair predictive algorithms
• Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models
• Fairness in Reinforcement Learning
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Cited by

• Unfairness towards subjective opinions in Machine Learning

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