Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
<|reference_start|>Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation: Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature generate recourse under the assumption of complete knowledge about the cost function. In real-world practice, subjects could have distinct preferences, leading to incomplete information about the underlying cost function of the subject. This paper proposes a two-step approach integrating preference learning into the recourse generation problem. In the first step, we design a question-answering framework to refine the confidence set of the Mahalanobis matrix cost of the subject sequentially. Then, we generate recourse by utilizing two methods: gradient-based and graph-based cost-adaptive recourse that ensures validity while considering the whole confidence set of the cost matrix. The numerical evaluation demonstrates the benefits of our approach over state-of-the-art baselines in delivering cost-efficient recourse recommendations.<|reference_end|>
@article{nguyen2024cost-adaptive,
    title={Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation},
    author={Duy Nguyen, Bao Nguyen, Viet Anh Nguyen},
    journal={arXiv preprint arXiv:2402.15073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15073},
    primaryClass={cs.LG}
}