The Explainability vs Accuracy Tradeoff

Explainability describes information about how a system works or why it produced an output; interpretability concerns the meaning of outputs in context. Predictive accuracy measures performance against chosen labels or outcomes. These qualities are related but not interchangeable. A model can be accurate but difficult to interpret, or easy to inspect but poorly validated. NIST’s AI Risk Management Framework treats validity and reliability, explainability and interpretability, privacy, fairness, and other qualities as distinct. It notes that tradeoffs can arise, including between predictive accuracy and interpretability. This is context-dependent, not a rule that simpler or more explainable models always perform worse. Some tasks allow strong performance and useful interpretability together; others involve constraints or use explanations after a complex model is trained. Define the operational goal before comparing models. A strong average score can conceal subgroup errors, poor calibration, or failures under distribution shift. An interpretable model can expose assumptions but still be biased or poorly validated. Post-hoc explanation tools may summarize behavior, yet an explanation is not causal proof or a guarantee that an individual result is correct. NIST AI RMF is voluntary guidance, not a binding certification. It recommends context-sensitive measurement over the AI lifecycle. Choose metrics that reflect consequences: predictive quality, subgroup performance, robustness, explanation fidelity, and whether the intended user can understand and act on the explanation. Document the selected balance and remaining uncertainty.