Machine learning models now automate decisions in applications where we may wish to provide recourse to adversely affected individuals. In practice, existing methods to provide recourse return actions that fail to account for latent characteristics that are not captured in the model (e.g., age, sex, marital status). In this paper, we study how the cost and feasibility of recourse can change across these latent groups. We introduce a notion of group-level plausibility to identify groups of individuals with a shared set of latent characteristics. We develop a general-purpose clustering procedure to identify groups from samples. Further, we propose a constrained optimization approach to learn models that equalize the cost of recourse over latent groups. We evaluate our approach through an empirical study on simulated and real-world datasets, showing that it can produce models that have better performance in terms of overall costs and feasibility at a group level.

Search engines are crucial in retrieving relevant items based on user-specified queries. A significant challenge arises when the buyer's vocabulary does not align with that of the seller, leading to a lack of sufficient recall or unsat-isfactory results. Such queries are referred to as “Null and Low” (N&L) queries which greatly hinder the overall user experience. Moreover, through analysis of user search behavioral data from a major e-commerce company, we have identified that approximately 29% of search queries exhibit multiple category interpretations, which we call “multi-faceted query interpretations”. In this study, we provide conceptual parallelism between the problem of N&L query reformulation and counterfactual explanation literature. To enhance the user experience for N&L queries, we propose a novel method that leverages the capabilities of a neural translation model to provide diverse and multiple reformulations. The proposed model demonstrated exceptional performance in our experiments, achieving an impressive 10% F-score improvement on the held-out test dataset with 5% improvement in relevance and a 100% increase in recall set size compared to a heuristic baseline, specifically for a set of N&L queries sampled from user traffic in eBay. By addressing the challenges of N&L queries and enabling the generation of diverse reformulations, our approach significantly enhances the overall search experience for users.

Recent work has connected adversarial attack methods and algorithmic recourse methods: both seek minimal changes to an input instance which alter a model’s classification decision. It has been shown that traditional adversarial training, which seeks to minimize a classifier’s susceptibility to malicious perturbations, increases the cost of generated recourse; with larger adversarial training radii correlating with higher recourse costs. From the perspective of algorithmic recourse, however, the appropriate adversarial training radius has always been unknown. Another recent line of work has motivated adversarial training with adaptive training radii to address the issue of instance-wise variable adversarial vulnerability, showing success in domains with unknown attack radii. This work studies the effects of adaptive adversarial training on algorithmic recourse costs. We establish that the improvements in model robustness induced by adaptive adversarial training show little effect on algorithmic recourse costs, providing a potential avenue for affordable robustness in domains where recoursability is critical.

The advent of machine learning in several critical fields, such as banking, healthcare, and criminal justice, has inspired research into improving robustness, trustworthiness, and transparency in the models. Actionable Recourse is one such tool that enables the negatively impacted users to receive a favorable outcome by providing recommendations of cost-efficient changes to their features. Current recourse methodologies optimize for proximity, sparsity, validity, and distance-based costs. Actionability takes both individual and group-level signals. A critical component of actionability is the consideration of User Preference to guide the recourse generation process. These preferences can take several forms, and we introduce three such preferences to capture the individual difficulty of user actions. Additionally, feasibility and plausibility should be considered as a fixed set of pre-specified constraints. We argue that plausibility draws strong signals from group-level population information, which must be considered to achieve low-cost recourses across protected groups. Recoursability is an active research area, and plausibility becomes an essential direction for further research.

Machine Learning’s proliferation in critical fields such as healthcare, banking, and criminal justice has motivated the creation of tools which ensure trust and transparency in ML models. One such tool is Actionable Recourse (AR) for negatively impacted users. AR describes recommendations of cost-efficient changes to a user’s actionable features to help them obtain favorable outcomes. Existing approaches for providing recourse optimize for properties such as proximity, sparsity, validity, and distance-based costs. However, an often-overlooked but crucial requirement for actionability is a consideration of User Preference to guide the recourse generation process. In this work, we attempt to capture user preferences via soft constraints in three simple forms: i) scoring continuous features, ii) bounding feature values and iii) ranking categorical features. Finally, we propose a gradient-based approach to identify User Preferred Actionable Recourse (UP-AR). We carried out extensive experiments to verify the effectiveness of our approach.

This study aims to investigate robust algorithms for stochastic multi-armed bandit problems with adversarially corrupted rewards. We consider a novel setup of stochastic bandits where the corruptions are sporadic and adaptive to the learner’s arm selection strategy with no upper limit on the total budget constraint. We first introduce an attacker model called Fractional Oracle Attack (FOA), and show its efficacy against the standard UCB and ε-greedy algorithms with sufficient conditions for its success under $\mathcal{O}(\log T)$ attack cost. We then present two robust algorithms Sample Dropout-UCB (SD-UCB) and Sample Dropout-ε-greedy (SD-εG) to defend against FOA. The core idea of our algorithms is to use reward dropout during sample mean estimation, therefore tolerating a significant amount of quantified corruption. Both the algorithms are significantly more robust when compared to contemporary roust algorithms, and achieves a regret at the order of $\mathcal{O}(\log T)$.