Explanationscan

Explanationscan is a conceptual framework and set of tools for auditing the explanations produced by machine learning models. It aims to assess whether explanations, derived from methods such as SHAP, LIME, local rule lists, or counterfactuals, faithfully reflect model behavior and are meaningful to users. The goal is to improve explainability quality and support governance, risk management, and accountability.

Purpose and scope: Explanationscan supports both local explanations for individual predictions and global explanations of overall model behavior. It helps practitioners verify faithfulness, detect inconsistencies, and uncover potential bias or fragility in explanations. The framework can incorporate domain knowledge, regulatory constraints, and provenance data to improve trust and defensibility.

Methodology: The typical workflow includes collecting explanations alongside predictions, aligning explanations with input features, testing robustness to perturbations, and comparing explanations against ground truth or domain rules. It uses metrics for faithfulness, stability, and plausibility, and generates audit reports or dashboards for governance, compliance, and model documentation.

Applications: Explanationscan is used in finance, healthcare, hiring, and other high-stakes settings where explainability is mandated or desired. It supports model cards, risk assessments, compliance reviews, and external audits by providing transparent traces from inputs to explanations.

Limitations: The concept relies on the quality of the underlying explainers; explanations can be misleading if the explainers are weak. There is potential for gaming explanations, additional computational overhead, and varying definitions of faithfulness across domains. It is not a substitute for robust, interpretable model design.

See also: Explainable AI, model interpretability, SHAP, LIME, counterfactual explanations, model cards.