The Impossibility Theorem of Machine Fairness -- A Causal Perspective

2020/07/12 by Kailash Karthik Saravanakumar, Saravanakumar, Kailash Karthik · 1 citation
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Blockchain Technology Applications and Security #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2007.06024

openalex publication_date 2020/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community. Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions. There are three prominent metrics of machine fairness used in the community, and it has been shown statistically that it is impossible to satisfy them all at the same time. This has led to an ambiguity with regards to the definition of fairness. In this report, a causal perspective to the impossibility theorem of fairness is presented along with a causal goal for machine fairness.

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