用逻辑编程生成可解释的反事实解释，让黑箱模型决策变透明。
CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP
- 基于规则学习提取模型逻辑，用目标导向逻辑编程生成反事实
- 确保修改特征时符合因果关系，结果更真实可信
- 适合需要可操作解释的信贷、招聘等高风险场景
机器学习模型在贷款审批、招聘等关键领域应用日益广泛，但其决策过程常为黑箱，难以解释。当结果不理想时，用户亟需理解如何改变才能获得期望结果。本文提出CoGS（Counterfactual Generation with s(CASP)），一种模型无关的反事实解释框架。CoGS利用目标导向答案集编程系统s(CASP)，结合规则型机器学习算法（如FOLD-SE）提取统计模型的底层逻辑，生成在因果关系约束下真实且一致的特征修改方案。通过逐步追踪从不良结果到理想结果的路径，CoGS提供可解释且可执行的改进建议。文中详述了CoGS框架并进行了评估。
原文摘要 · Abstract (English)
Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, as individuals need explanations to understand decisions, primarily if the decisions result in an undesired outcome. Our work introduces CoGS (Counterfactual Generation with s(CASP)), a model-agnostic framework capable of generating counterfactual explanations for classification models. CoGS leverages the goal-directed Answer Set Programming system s(CASP) to compute realistic and causally consistent modifications to feature values, accounting for causal dependencies between them. By using rule-based machine learning algorithms (RBML), notably the FOLD-SE algorithm, CoGS extracts the underlying logic of a statistical model to generate counterfactual solutions. By tracing a step-by-step path from an undesired outcome to a desired one, CoGS offers interpretable and actionable explanations of the changes required to achieve the desired outcome. We present details of the CoGS framework along with its evaluation.
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