With the growing use of machine learning (ML) models in critical domains such as finance and healthcare, the need to offer recourse for those adversely affected by the decisions of ML models has become more important; individuals ought to be provided with recommendations on actions to take for improving their situation and thus receiving a favorable decision. Prior work on sequential algorithmic recourse -- which recommends a series of changes -- focuses on action feasibility and uses the proximity of feature changes to determine action costs. However, the uncertainties of feature changes and the risk of higher than average costs in recourse have not been considered. It is undesirable if a recourse could (with some probability) result in a worse situation from which recovery requires an extremely high cost. It is essential to incorporate risks when computing and evaluating recourse. We call the recourse computed with such risk considerations as Safer Algorithmic Recourse (SafeAR). The objective is to empower people to choose a recourse based on their risk tolerance. In this work, we discuss and show how existing recourse desiderata can fail to capture the risk of higher costs. We present a method to compute recourse policies that consider variability in cost and connect algorithmic recourse literature with risk-sensitive reinforcement learning. We also adopt measures "Value at Risk" and "Conditional Value at Risk" from the financial literature to summarize risk concisely. We apply our method to two real-world datasets and compare policies with different risk-aversion levels using risk measures and recourse desiderata (sparsity and proximity).

翻译：随着机器学习模型在金融和医疗等关键领域的广泛应用，为受其决策不利影响的个体提供追索途径的需求日益迫切——个人应当获得改善自身处境、进而获得有利决策的行动建议。现有序列化算法追索研究（推荐一系列变更措施）侧重于行动可行性，并通过特征变更的邻近性衡量行动成本。然而，特征变更的不确定性以及追索中可能产生的超平均成本风险尚未被考虑。若某项追索方案存在（以一定概率）导致更糟处境、且恢复需付出极高代价的风险，则其不可取。在计算与评估追索方案时必须纳入风险因素。我们将此类考虑风险计算的追索方案称为更安全算法追索（SafeAR），其目标在于使个人能够根据自身风险承受能力选择追索策略。本研究论证并揭示了现有追索准则无法捕捉高成本风险的问题，提出了一种考虑成本变异性的追索策略计算方法，将算法追索文献与风险敏感强化学习相联系。我们借鉴金融领域的"风险价值"与"条件风险价值"指标对风险进行简明定量，并在两个真实数据集上应用该方法，通过风险度量及追索准则（稀疏性与邻近性）比较不同风险规避水平的策略。