---
title: "Interpretability"
description: "In NIST's framing, the meaning of a system's output in the context of its purpose: why a decision was made and what it means to the user."
canonical: https://aigovernanceengineer.com/glossary/interpretability
author: "Jorge García Aibar"
license: "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)"
doi: https://doi.org/10.5281/zenodo.22956197
version: "0.5.0"
updated: 2026-09-24
---

# Interpretability

In NIST's framing, the meaning of a system's output in the context of its purpose: why a decision was made and what it means to the user [1]. An inherently interpretable model, such as a scorecard or a shallow tree, is its own explanation.

- Developed in: [ch. 16, Transparency, interpretability and explainability](https://aigovernanceengineer.com/bok/fairness-and-explainability#transparency-interpretability-and-explainability); [ch. 16, Interpretable by design or explained after the fact](https://aigovernanceengineer.com/bok/fairness-and-explainability#interpretable-by-design-or-explained-after-the-fact)
- Chapters: [ch. 16, Fairness & XAI](https://aigovernanceengineer.com/bok/fairness-and-explainability)
- Contrast with: [Explainability](https://aigovernanceengineer.com/glossary/explainability)
- In the glossary chapter: https://aigovernanceengineer.com/bok/glossary#t-interpretability

## Sources

[1] Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (risk tolerance and residual risk; seven trustworthy characteristics; transparency answers "what happened", explainability "how", interpretability "why"; MAP 1.1 intended purposes; MANAGE 1.1 go/no-go determination; profiles). NIST. 2023-01-26. https://doi.org/10.6028/NIST.AI.100-1 (verified: primary)