Machine learning models are increasingly used in areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, and individuals need explanations to understand decisions, especially for the ones not desired by the user. Ethical and legal considerations require informing individuals of changes in input attribute values (features) that could lead to a desired outcome for the user. Our work aims to generate counterfactual explanations by considering causal dependencies between features. We present the CoGS (Counterfactual Generation with s(CASP)) framework that utilizes the goal-directed Answer Set Programming system s(CASP) to generate counterfactuals from rule-based machine learning models, specifically the FOLD-SE algorithm. CoGS computes realistic and causally consistent changes to attribute values taking causal dependencies between them into account. It finds a path from an undesired outcome to a desired one using counterfactuals. We present details of the CoGS framework along with its evaluation.

翻译：机器学习模型在贷款审批和招聘等领域的应用日益广泛，但其决策过程往往如同黑箱，缺乏透明度。可解释性至关重要，用户需要理解模型决策的依据，尤其是针对不符合自身期望的结果。出于伦理与法律考量，必须向用户说明如何调整输入属性值（特征）才能获得期望的结果。本研究旨在通过考虑特征间的因果依赖关系来生成反事实解释。我们提出CoGS（基于s(CASP)的反事实生成）框架，该框架利用目标导向的答案集编程系统s(CASP)，从基于规则的机器学习模型（特别是FOLD-SE算法）中生成反事实。CoGS通过考虑特征间的因果依赖关系，计算属性值在现实层面与因果逻辑上均一致的调整方案，从而构建一条从未期望结果通往期望结果的反事实路径。本文详细阐述了CoGS框架的设计原理并进行了系统评估。