As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption that the underlying predictive model does not change. However, in practice, models are often regularly updated for a variety of reasons (e.g., dataset shifts), thereby rendering previously prescribed recourses ineffective. To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. To the best of our knowledge, this work proposes the first solution to this critical problem. We also carry out detailed theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) we derive a lower bound on the probability of invalidation of recourses generated by existing approaches which are not robust to model shifts. 2) we prove that the additional cost incurred due to the robust recourses output by our framework is bounded. Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework and supports our theoretical findings.

翻译：随着预测模型被越来越多地用于高层决策(例如贷款审批),人们越来越关注为受影响的个人提供求助手段的后期特别技术,这些技术在基本预测模型没有变化的假设下产生追索权,但在实践中,由于各种原因(例如数据集转换),模型经常定期更新,从而使先前规定的追索无效。为了解决这一问题,我们提议了一个新的框架,即ROBust Algorithmic Reclement(ROAR),利用对抗性培训来寻找对模式转变具有活力的追索权。根据我们的知识,这项工作提出了解决这一关键问题的第一个办法。我们还进行了详细的理论分析,强调构建对模式转变具有活力的追索权的重要性:1)我们从现有方法产生的追索权无效的可能性中得出了一个较低的界限。我们证明,由于我们框架的强有力追索权产出而产生的额外费用是受约束的。对多种合成和真实世界数据集的实验性评估显示了拟议框架的功效并支持我们的理论结论。