Socially Fair Matching: Exact and Approximation Algorithms
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-38906-1_6
Reference23 articles.
1. Abbasi, M., Bhaskara, A., Venkatasubramanian, S.: Fair clustering via equitable group representations. In: Elish, M.C., Isaac, W., Zemel, R.S. (eds.) FAccT 2021: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event/Toronto, Canada, 3–10 March 2021, pp. 504–514. ACM (2021)
2. Barketau, M., Pesch, E., Shafransky, Y.: Minimizing maximum weight of subsets of a maximum matching in a bipartite graph. Discrete Appl. Math. 196, 4–19 (2015)
3. Berger, A., Bonifaci, V., Grandoni, F., Schäfer, G.: Budgeted matching and budgeted matroid intersection via the gasoline puzzle. Math. Program. 128(1–2), 355–372 (2011). https://doi.org/10.1007/s10107-009-0307-4
4. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)
5. Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017, pp. 797–806. ACM (2017)
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