Robust Recourse for Binary Allocation Problems
Abstract
We present the problem of algorithmic recourse for the setting of binary allocation problems. In this setting, the optimal allocation does not depend only on the prediction model and the individual's features, but also on the current available resources, decision maker's objective and other individuals currently applying for the resource. Specifically, we focus on 0-1 knapsack problems and in particular the use case of lending. We first provide a method for generating counterfactual explanations and then address the problem of recourse invalidation due to changes in allocation variables. Finally, we empirically compare our method with perturbation-robust recourse and show that our method can provide higher validity at a lower cost.
Cite
Text
Segal et al. "Robust Recourse for Binary Allocation Problems." NeurIPS 2023 Workshops: XAIA, 2023.Markdown
[Segal et al. "Robust Recourse for Binary Allocation Problems." NeurIPS 2023 Workshops: XAIA, 2023.](https://mlanthology.org/neuripsw/2023/segal2023neuripsw-robust/)BibTeX
@inproceedings{segal2023neuripsw-robust,
title = {{Robust Recourse for Binary Allocation Problems}},
author = {Segal, Meirav and George, Anne-Marie and Yu, Ingrid and Dimitrakakis, Christos},
booktitle = {NeurIPS 2023 Workshops: XAIA},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/segal2023neuripsw-robust/}
}