Explainability and Interpretability are critical Trustworthiness Characteristics in the NIST AI RMF that address the comprehensibility of an AI system's internal logic and outputs. They enable users, developers, and affected individuals to understand not just what decision an AI system made, but how and why it arrived at that result.

While often used interchangeably, a useful distinction exists: interpretability is a property of the model itself—how inherently understandable its structure is (e.g., a short decision tree). Explainability is often an external technique applied to complex models to generate post-hoc reasons for their behavior (e.g., using SHAP values to highlight important features). Together, they bridge the gap between the complex statistical patterns learned by AI and the human need for causal reasoning and justification. This understanding is essential for debugging, for validating that the system uses appropriate reasoning, for enabling meaningful human oversight, and for providing recourse to affected individuals.

Achieving explainability and interpretability involves a spectrum of technical approaches:

Model Selection for Interpretability: Choosing inherently interpretable model architectures (like linear models or rule-based systems) when the task and performance requirements allow.

Post-Hoc Explanation Techniques: Applying methods like LIME (Local Interpretable Model-agnostic Explanations) or SHAP to attribute an output to specific input features, or using counterfactual explanations ("the output would have been different if input X had been Y").

Example-Based Explanations: Providing users with similar historical cases from the training data that led to a similar output, helping to contextualize the decision.

Uncertainty Quantification: Communicating the model's confidence level in its prediction, which is itself a critical piece of explanatory information for a human overseer.

Regulatory Context: The EU AI Act mandates explainability as a requirement for high-risk systems, stating instructions for use must enable the deployer to interpret the system's output (Article 13). This legal requirement drives the adoption of Explainable AI (XAI) techniques. NIST's focus on this characteristic provides the technical framework for meeting such regulatory demands.

Enabler of Trust and Oversight: Explainability is not merely a technical feature; it is a prerequisite for trust and control. In high-risk domains like healthcare or finance, the inability to explain a decision can render an AI system unusable, regardless of its accuracy. It is the key that unlocks human judgment, allowing people to validate, challenge, and responsibly act upon AI-generated insights.