# The Impossibility Theorem of Machine Fairness -- A Causal Perspective

arXiv: https://arxiv.org/abs/2007.06024 · HTML: https://mlsanity.com/paper/2007.06024/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Pushing the limits of fairness impossibility: Who's the fairest of them all?](https://mlsanity.com/paper/2208.12606/) (2022)
- [Impossibility Results in AI: A Survey](https://mlsanity.com/paper/2109.00484/) (2021)
- [Consistent Range Approximation for Fair Predictive Modeling](https://mlsanity.com/paper/2212.10839/) (2022)
- [Towards Automatic Bias Detection in Knowledge Graphs](https://mlsanity.com/paper/2109.10697/) (2021)
- [Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases](https://mlsanity.com/paper/2206.12540/) (2022)
- [Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms](https://mlsanity.com/paper/2311.06826/) (2023)
- [Standardized Interpretable Fairness Measures for Continuous Risk Scores](https://mlsanity.com/paper/2308.11375/) (2023)
- [AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias](https://mlsanity.com/paper/2407.19655/) (2024)
- [SoK: What Makes Private Learning Unfair?](https://mlsanity.com/paper/2501.14414/) (2025)
- [Hard Choices and Hard Limits for Artificial Intelligence](https://mlsanity.com/paper/2105.07852/) (2021)
- [Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements](https://mlsanity.com/paper/2306.10043/) (2023)
- ["Patriarchy Hurts Men Too." Does Your Model Agree? A Discussion on Fairness Assumptions](https://mlsanity.com/paper/2408.00330/) (2024)
- [Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space](https://mlsanity.com/paper/2510.22209/) (2025)

## 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.