What is Explainable AI (XAI)?

Summary

Explainable AI (XAI) comprises techniques that help people understand how AI systems produce specific outputs, particularly when machine-learning and deep-learning models operate as black boxes. It distinguishes intrinsically interpretable models, such as decision trees and linear or logistic regression, from post-hoc methods including SHAP, LIME, counterfactuals, saliency maps and Grad-CAM. A typical workflow selects a model and prediction, applies a method suited to the model and audience, reviews outputs such as feature scores or heatmaps, and uses them to assess accuracy, fairness, reliability and compliance. The article stresses that post-hoc explanations are approximations rather than definitive proof, so teams should validate them with domain expertise and, where appropriate, combine methods; MLflow and Unity Catalog can preserve explanation artifacts, lineage and auditability.

Context

As AI systems take on consequential decisions in areas such as lending, hiring, healthcare, fraud detection and insurance, their outputs can be difficult to trace or defend. The source identifies trust, debugging, compliance, fairness and ongoing monitoring as reasons to make model behavior understandable.

Approach / What changed

The source categorizes XAI into intrinsically interpretable models, post-hoc explanation methods and visualization-based methods. It describes choosing a model and prediction, selecting a suitable technique, reviewing the resulting explanation, and matching its detail to the audience. MLflow and Unity Catalog are presented as ways to retain explanation artifacts, lineage, versioning and audit logs.

Takeaways

• Intrinsically interpretable models expose their logic directly, but they can be less accurate than complex models on difficult tasks such as image recognition or language understanding.
• SHAP provides local and global feature contributions, while LIME creates a simple surrogate model for one prediction; both are post-hoc approximations rather than exact reconstructions of model computation.
• XAI explanations should be treated as evidence rather than conclusive proof. Validating them with domain expertise and combining multiple methods can provide a more reliable picture.

Related reading