Fairness & Bias in ML Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Influential papers on fairness, bias, and equity in machine learning, covering metrics, mitigation, and empirical studies.

Fairness & Bias in ML: 10 key papers

1. Equality of Opportunity in Supervised Learning
   Hardt et al. arXiv 2016.
2. Fairness Through Awareness
   Dwork et al. arXiv 2011.
3. A Survey on Bias and Fairness in Machine Learning
   Mehrabi et al. arXiv 2019.
4. Inherent Trade-Offs in the Fair Determination of Risk Scores
   Kleinberg et al. arXiv 2016.
5. Fairness Constraints: Mechanisms for Fair Classification
   Zafar et al. arXiv 2015.
6. Learning Fair Representations
   Zemel et al. International Conference on Machine Learning 2013.
7. Counterfactual Fairness
   Kusner et al. arXiv 2017.
8. Delayed Impact of Fair Machine Learning
   Liu et al. arXiv 2018.
9. On Fairness and Calibration
   Pleiss et al. arXiv 2017.
10. The Frontiers of Fairness in Machine Learning
    Chouldechova and Roth. arXiv 2018.

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