Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
This work addresses the challenge of personalized recourse recommendations in machine learning for subjects with varying preferences, representing an incremental improvement over existing methods.
The paper tackles the problem of generating algorithmic recourse when subjects have distinct preferences, leading to incomplete knowledge of cost functions, by proposing a two-step approach integrating preference learning and cost-adaptive recourse generation, resulting in improved cost-efficiency over state-of-the-art baselines.
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.