CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP
This work addresses the need for transparency in AI decision-making for individuals affected by automated systems, though it appears incremental by combining existing techniques like s(CASP) and FOLD-SE for explanation generation.
The authors tackled the problem of generating interpretable counterfactual explanations for black-box machine learning models in critical applications like loan approvals, by introducing CoGS, a model-agnostic framework that uses s(CASP) and rule-based learning to produce causally consistent modifications, achieving realistic and actionable explanations.
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.