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

References

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  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. In 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. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. In Advances in Neural Information Processing Systems (NeurIPS).
  11. Zhang, J., & Bareinboim, E. (2018). Fairness in decision-making—the causal explanation formula. In AAAI Conference on Artificial Intelligence.
  12. Loftus, J. R., Russell, C., Kusner, M. J., & Silva, R. (2018). Causal reasoning for algorithmic fairness. ArXiv:1805.05859.
  13. Kusner, M. J., Russell, C., Loftus, J. R., & Silva, R. (2018). Causal Interventions for Fairness. ArXiv:1806.02380.
  14. Madras, D., Creager, E., Pitassi, T., & Zemel, R. (2019). Fairness through causal awareness: Learning causal latent-variable models for biased data. In Conference on Fairness, Accountability, and Transparency.