Explainability vs Interpretability in AI

Working definitions

NIST AI RMF 1.0 separates the two terms by object. Explainability refers to a representation of the mechanisms underlying an AI system's operation. Interpretability refers to the meaning of the system's output in the context of its designed functional purpose. NIST treats transparency as a third, distinct characteristic, and states that the three support each other.

The EU AI Act uses the vocabulary differently. Its recital text defines transparency as AI systems being developed and used so as to allow appropriate traceability and explainability. It also covers making people aware they are interacting with an AI system, informing deployers of capabilities and limitations, and informing affected persons of their rights. Under that text, explainability sits inside transparency rather than beside it.

The practical consequence is that a requirement saying 'the system must be explainable' is underspecified. A reviewer, a vendor and a regulator can each read it differently. Naming the framework and the definition in use is the first step toward an acceptance criterion that can be tested.

Explainability and interpretability side by side

The comparison below uses NIST's definitions. NIST ties both characteristics to the audience, so the descriptions should be read as depending on who needs the explanation.

• Object: Explainability: the mechanisms underlying how the system operates. Interpretability: the meaning of an output given the system's designed purpose.
• Question answered: Explainability: how does the system work? Interpretability: why did it make this prediction or recommendation?
• Typical scope: Explainability: often global, covering behavior across all outputs. Interpretability: often local, covering a single result. NIST-hosted guidance distinguishes local and global explanation.
• Audience: Explainability: descriptions tailored to the user's role, knowledge and skill level. Interpretability: a communicated description of why a particular output was produced, for operators, overseers and users.
• Common methods: Explainability: model documentation, simpler model structures, mechanism descriptions. Interpretability: output-level rationales, including post-hoc explanation methods.
• Main limitation: Explainability: a mechanism description may not scale to complex systems. Interpretability: a rationale for one output may not describe the system as a whole.

Interpretable models and post-hoc explanations

An inherently interpretable model is one whose structure can be read directly, such as a small decision tree or a sparse linear model. The explanation is the model. A post-hoc explanation is generated after the fact for a model that is not readable in this way. Feature attribution is a common example: it estimates which inputs contributed most to a given output.

Both approaches have limits. NIST-hosted guidance notes that in more complex cases an interpretable model may lose much of its global as well as its local explainability. A model that is interpretable at small scale should not be assumed to remain so once it grows in size or interaction complexity.

A post-hoc explanation is a derived account of behavior, not the computation itself. It may be a useful approximation, but whether it reflects what the system actually did is an empirical question that has to be tested against the real system. This matters most for large language models and other generative systems. Their outputs are open-ended, and an attribution to input features may not translate into a rationale a reviewer can act on. Treat any explanation claim from a vendor as a claim to verify, and ask what evidence shows the explanation matches actual system behavior.

Three questions enterprises conflate

Much of the ambiguity in model selection, vendor review and audit preparation comes from combining three different questions. The first is why a model produced a particular output. That is a local interpretability need, and it is usually answered with an output-level rationale. The second is how the model works internally. That is a global explainability need, answered by an inherently interpretable design or by a description of mechanisms suited to the audience.

The third is what the system was permitted to do and what it actually did. This is not a property of the model. It concerns the deployed system, its policies and its recorded behavior. The EU AI Act's obligations for high-risk systems reflect this distinction. They require a risk management system that is established, implemented, documented and maintained, and technical documentation that is drawn up before the system is placed on the market or put into service and kept up to date. These are documentation and process duties, not model-interpretability requirements.

Because the three questions have different answers, they need different owners. Assign explanation content, technical documentation and risk-management records separately, and make sure each has a named accountable party. A single owner for all three tends to leave at least one under-specified.

Where runtime policy enforcement and audit-ready evidence fit

Model-level explanation does not tell a regulator or auditor what an AI system was allowed to do in production, or what it did. That question is answered at runtime, at the level of individual interactions and tool calls.

Trussed addresses this need. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants, so policy is enforced at runtime, not reviewed after the fact. Each interaction or tool call can be allowed, redacted or blocked, and Trussed can also rate limit or escalate for human approval. That enforcement generates continuous audit-ready evidence mapped to frameworks including HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Deployment is self-managed in the customer cloud, with configurable retention and data residency.

This is a complement to model-level explanation, not a replacement. Trussed does not produce model explanations, model lineage or end-to-end tracing of agentic workflows. A regulated enterprise still needs its interpretability and explainability requirements defined as above. Runtime evidence covers the separate record of permitted and actual behavior.