Fairness Under Distribution Shift: From Static Metrics to Adaptive Assurance

This review examines fairness assurance under distribution shift. The organizing question is how fairness evidence should be updated when populations, labels, incentives, or deployment conditions change. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to assuming a fairness metric remains valid after the data-generating process changes. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in high-impact predictive and ranking systems.

1. Acevedo, N., Cortez, C., Brooks, C., Kizilcec, R. F., & Yu, R. (2024). Fairness Hub Technical Briefs: Mitigation Strategies of Distribution Shift. Scholarly publication. https://doi.org/10.35542/osf.io/pvbmt DOI
2. An, B., Che, Z., Ding, M., & Huang, F. (2022). Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2206.12796 DOI
3. Casalicchio, V., Manzolini, G., Prina, M. G., & Moser, D. (2022). From investment optimization to fair benefit distribution in renewable energy community modelling. Applied Energy, 310, 118447. https://doi.org/10.1016/j.apenergy.2021.118447 DOI
4. Chen, Y., Raab, R., Wang, J., & Liu, Y. (2022). Fairness Transferability Subject to Bounded Distribution Shift. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2206.00129 DOI
5. Jiang, Z., Han, X., Jin, H., Wang, G., Chen, R., Zou, N., & Hu, X. (2023). Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2303.03300 DOI
6. Liu, M. Z., Procopiou, A. T., Petrou, K., Ochoa, L. F., Langstaff, T., Harding, J., & Theunissen, J. (2020). On the Fairness of PV Curtailment Schemes in Residential Distribution Networks. IEEE Transactions on Smart Grid, 11(5), 4502-4512. https://doi.org/10.1109/tsg.2020.2983771 DOI
7. Rezaei, A., Liu, A., Memarrast, O., & Ziebart, B. D. (2021). Robust Fairness Under Covariate Shift. Scholarly publication. https://doi.org/10.1609/aaai.v35i11.17135 DOI
8. Shao, M., Li, D., Zhao, C., Wu, X., Lin, Y., & Tian, Q. (2024). Supervised Algorithmic Fairness in Distribution Shifts: A Survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.01327 DOI
9. Wang, H., Hong, J., Zhou, J., & Wang, Z. (2022). How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. PubMed, 2023. https://doi.org/10.48550/arxiv.2207.01168 DOI
10. Wolbeck, L., Kliewer, N., & Marques, I. (2020). Fair shift change penalization scheme for nurse rescheduling problems. European Journal of Operational Research, 284(3), 1121-1135. https://doi.org/10.1016/j.ejor.2020.01.042 DOI