mlsanityData to 7 Oct 2026

The Impossibility Theorem of Machine Fairness -- A Causal Perspective

arXiv 2007.06024 ↗First posted 12 Jul 2020cs.LG cs.AI stat.MLcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 2; 2025: 2.

13 papers.

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  1. 2022Pushing the limits of fairness impossibility: Who's the fairest of them all?cited by 15
  2. 2021Impossibility Results in AI: A Surveycited by 13
  3. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  4. 2021Towards Automatic Bias Detection in Knowledge Graphscited by 6
  5. 2022Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biasescited by 5
  6. 2023Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithmscited by 5
  7. 2023Standardized Interpretable Fairness Measures for Continuous Risk Scorescited by 3
  8. 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
  9. 2025SoK: What Makes Private Learning Unfair?cited by 3
  10. 2021Hard Choices and Hard Limits for Artificial Intelligencecited by 2
  11. 2023Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancementscited by 2
  12. 2024"Patriarchy Hurts Men Too." Does Your Model Agree? A Discussion on Fairness Assumptionscited by 0
1 more
  1. 2025Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Spacecited by 0