Learning Recourse Costs from Pairwise Feature Comparisons

This paper presents a novel technique for incorporating user input when learning and inferring In high stakes decision settings such as credit scoring, processing user preferences. When trying to provide users bail applications, or making hiring decisions, applicants of black-box machine learning models with actionable often seek recourse to correct unfavourable predicted recourse, we often wish to incorporate outcomes for the future. In these scenarios, since there their personal preferences about the ease of modifying can be multiple possible recourses for each individual, feasibility each individual feature. These recourse considerations, user preferences, and heuristics to finding algorithms usually require an exhaustive minimize the size of the proposed modifications are used to set of tuples associating each feature to its cost guide the search for appropriate recourses (Poyiadzi et al., of modification. Since it is hard to obtain such 2020; Pawelczyk et al., 2020; Joshi et al., 2019). Recourse costs by directly surveying humans, in this paper, search algorithms thus return the best possible recourse we propose the use of the Bradley-Terry model based on these considerations by performing a search over to automatically infer feature-wise costs using the feature-space of the model.