Counterfactual interventions are a powerful tool to explain the decisions of a black-box decision process and to enable algorithmic recourse. They are a sequence of actions that, if performed by a user, can overturn an unfavourable decision made by an automated decision system. However, most of the current methods provide interventions without considering the user's preferences. In this work, we propose a shift of paradigm by providing a novel formalization which considers the user as an active part of the process rather than a mere target. Following the preference elicitation setting, we introduce the first human-in-the-loop approach to perform algorithmic recourse. We also present a polynomial procedure to ask questions which maximize the Expected Utility of Selection (EUS), a measure of the utility of the choice set that accounts for the uncertainty with respect to both the model and the user response. We use it to iteratively refine our cost estimates in a Bayesian fashion. We integrate this preference elicitation strategy into a reinforcement learning agent coupled with Monte Carlo Tree Search for the efficient exploration, so as to provide personalized interventions achieving algorithmic recourse. An experimental evaluation of synthetic and real-world datasets shows that a handful of queries allows for achieving a substantial reduction in the cost of interventions with respect to user-independent alternatives.

翻译：反事实干预是解释黑箱决策过程并实现算法补救的有力工具。它由一系列行动组成，用户执行这些行动后，可以逆转自动化决策系统做出的不利决定。然而，当前大多数方法在提供干预措施时并未考虑用户的偏好。本研究提出一种范式转换，通过引入全新形式化框架将用户视为过程的主动参与者而非单纯的目标对象。基于偏好诱导情景，我们首次提出人机协同的算法补救方法。同时设计了一种多项式时间的提问策略，通过最大化选择集的期望效用（EUS）——一种综合考虑模型不确定性和用户响应不确定性的选择集效用度量——来优化提问过程。我们采用贝叶斯方法迭代优化成本估计，并将该偏好诱导策略集成到基于蒙特卡洛树搜索的强化学习智能体中实现高效探索，从而提供个性化的算法补救干预。在合成数据集与真实世界数据集上的实验表明，仅需少量查询即可显著降低干预成本，效果优于不依赖用户偏好的替代方案。