Explainable machine learning (ML) has gained traction in recent years due to the increasing adoption of ML-based systems in many sectors. Algorithmic Recourses (ARs) provide "what if" feedback of the form "if an input datapoint were x' instead of x, then an ML-based system's output would be y' instead of y." ARs are attractive due to their actionable feedback, amenability to existing legal frameworks, and fidelity to the underlying ML model. Yet, current AR approaches are single shot -- that is, they assume x can change to x' in a single time period. We propose a novel stochastic-control-based approach that generates sequential ARs, that is, ARs that allow x to move stochastically and sequentially across intermediate states to a final state x'. Our approach is model agnostic and black box. Furthermore, the calculation of ARs is amortized such that once trained, it applies to multiple datapoints without the need for re-optimization. In addition to these primary characteristics, our approach admits optional desiderata such as adherence to the data manifold, respect for causal relations, and sparsity -- identified by past research as desirable properties of ARs. We evaluate our approach using three real-world datasets and show successful generation of sequential ARs that respect other recourse desiderata.
翻译:近年来,由于在许多部门越来越多地采用基于 ML 的系统,可解释的机器学习(ML ) 获得了牵引力。 演算法(ARs) 提供了“ 如果” 形式的“ 如果输入数据点是 x 而不是 x ” 反馈, 那么基于 ML 的系统输出将是 y 而不是 y 。 ARs 由于其可操作的反馈、 对现有法律框架的兼容性和对基础 ML 模式的忠诚性而具有吸引力。 然而, 目前的AR 方法是一次性的, 也就是说, 他们假设x 可以在一个单一的时期内改变为 x 。 我们提出一种新的基于分析控制的方法, 产生相继的 ARs, 也就是说, ARs 允许 x 在中间状态之间按顺序和顺序移动一个输出, 而不是 y' y。 我们的方法是模范的黑盒子。 此外, ARs的计算方法被重新分解, 适用于多个数据点, 而不需要重新优化。 除了这些主要特点之外, 我们的方法还承认一种基于 选择的随机序列 方法, 以选择性的方法, 显示我们所认定的正确的数据的顺序 。