Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation

• URL: http://arxiv.org/abs/2402.15073v1
• Date: Fri, 23 Feb 2024 03:27:17 GMT
• Title: Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
• Authors: Duy Nguyen, Bao Nguyen, Viet Anh Nguyen
• Abstract summary: Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. This paper proposes a two-step approach integrating preference learning into the recourse generation problem.
• Score: 18.423687983628145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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