A repository of resources for Fairness in Machine Learning.

Core Concepts in Fairness

Sampling for Fairness

Encryption for Fairness

Causal Inference in Fairness

Other Code Repositories



  1. Hashimoto, T. B., Srivastava, M., Namkoong, H., & Liang, P. (2018). Fairness without demographics in repeated loss minimization. International Conference on Machine Learning (ICML).
  2. Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2018). Delayed impact of fair machine learning. ArXiv:1803.04383.
  3. Kallus, N., & Zhou, A. (2018). Residual unfairness in fair machine learning from prejudiced data. ArXiv:1806.02887.
  4. Agarwal, A., Beygelzimer, A., Dudı́k Miroslav, Langford, J., & Wallach, H. (2018). A reductions approach to fair classification. ArXiv:1803.02453.
  5. Komiyama, J., Takeda, A., Honda, J., & Shimao, H. (2018). Nonconvex optimization for regression with fairness constraints. International Conference on Machine Learning (ICML).
  6. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A Survey on Bias and Fairness in Machine Learning. ArXiv:1908.09635.
  7. Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. ArXiv:1808.00023.
  8. Celis, L. E., Keswani, V., Straszak, D., Deshpande, A., Kathuria, T., & Vishnoi, N. K. (2018). Fair and diverse DPP-based data summarization. ArXiv Preprint ArXiv:1802.04023.
  9. Amini, A., Soleimany, A., Schwarting, W., Bhatia, S., & Rus, D. (2019). Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure.
  10. Kilbertus, N., Gascón, A., Kusner, M. J., Veale, M., Gummadi, K. P., & Weller, A. (2018). Blind justice: Fairness with encrypted sensitive attributes. ArXiv:1806.03281.
  11. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems (NeurIPS).
  12. Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017). Avoiding discrimination through causal reasoning. Advances in Neural Information Processing Systems, 656–666.
  13. Zhang, J., & Bareinboim, E. (2018). Fairness in decision-making—the causal explanation formula. AAAI Conference on Artificial Intelligence.
  14. Loftus, J. R., Russell, C., Kusner, M. J., & Silva, R. (2018). Causal reasoning for algorithmic fairness. ArXiv:1805.05859.
  15. Kusner, M. J., Russell, C., Loftus, J. R., & Silva, R. (2018). Causal Interventions for Fairness. ArXiv:1806.02380.
  16. Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2019). Fairness through causal awareness: Learning causal latent-variable models for biased data. Conference on Fairness, Accountability, and Transparency.