A repository of resources for Fairness in Machine Learning.

Core Concepts in Fairness

• (Hashimoto et al., 2018)

• (Liu et al., 2018)

• (Kallus & Zhou, 2018)

• (Agarwal et al., 2018)

• (Komiyama et al., 2018)

• (Mehrabi et al., 2019)

• (Corbett-Davies & Goel, 2018)

Sampling for Fairness

• (Celis et al., 2018)

• (Amini et al., 2019)

Encryption for Fairness

• (Kilbertus et al., 2018)

Causal Inference in Fairness

• (Kusner et al., 2017)

• (Kilbertus et al., 2017)

• (Zhang & Bareinboim, 2018)

• (Loftus et al., 2018)

• (Kusner et al., 2018)

• (Madras et al., 2019)

Other Code Repositories

• AI 360 Toolkit

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. 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.