Classification
AI Fundamentals
Overview
Explainability and interpretability are related but distinct ideas used to make AI systems understandable. Interpretability refers to models whose internal mechanisms are sufficiently simple or structured so that a knowledgeable person can follow how inputs map to outputs (e.g., linear regression with transparent coefficients, shallow decision trees, monotonic gradient-boosted trees). Explainability refers to post-hoc tools and procedures that generate human-understandable reasons for predictions made by complex or opaque models (e.g., SHAP, LIME, counterfactual explanations, saliency maps). In practice, organizations mix both: they may choose inherently interpretable models where stakes are high, or use explainability techniques to probe high-performing but opaque models. Trade-offs include performance versus transparency, local versus global faithfulness, user comprehension, and robustness to manipulation. A limitation is that some post-hoc explanations can be unstable or misleading if they are not faithful to the model's true decision logic, which can create false confidence or compliance risk when explanations are used for accountability.
Governance Context
Regulatory frameworks expect meaningful transparency calibrated to risk. The EU AI Act requires technical documentation, traceability, and information for users enabling them to interpret system outputs; high-risk systems must provide appropriate human oversight and post-market documentation updates. GDPR Articles 13-15 and 22 underpin transparency duties and, in certain contexts, require providing meaningful information about the logic involved in automated decisions. NIST AI RMF calls for documentation of model assumptions and explanation limits, and for evaluation of explanation quality with target users. Two concrete obligations are: (1) select and justify an explanation strategy that matches the use case and audience (e.g., SHAP summaries for risk officers; counterfactuals for impacted individuals), documenting limitations and validation results; and (2) implement human-in-the-loop procedures so that explanations are reviewed for consistency, with metrics (stability, fidelity, usability) monitored over time and recorded in model cards.
Ethical & Societal Implications
Explanations influence trust, recourse, and fairness. Poorly designed or unfaithful explanations can legitimize harmful outcomes, while overly technical disclosures can overwhelm affected individuals. Clear, accessible explanations support contestability and informed consent, but may expose intellectual property or enable gaming. Careful design must balance disclosure with security and privacy, and ensure explanations do not shift responsibility away from accountable human decision-makers.
Key Takeaways
Interpretability is about transparent models; explainability explains opaque models.; Choose explanation methods that match risk, audience, and context.; Evaluate explanation fidelity, stability, and usability-not just accuracy.; EU AI Act and GDPR require meaningful, understandable information.; Documentation and human oversight are essential for accountable explanations.