---
title: "Explainability"
description: "In NIST's framing, a representation of the mechanisms behind a system's operation: how a decision was made."
canonical: https://aigovernanceengineer.com/glossary/explainability
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
---

# Explainability

In NIST's framing, a representation of the mechanisms behind a system's operation: how a decision was made [1]. In practice a per-decision explanation such as feature attributions, reason codes or a counterfactual.

- Developed in: [ch. 16, Transparency, interpretability and explainability](https://aigovernanceengineer.com/bok/fairness-and-explainability#transparency-interpretability-and-explainability); [ch. 11, Contrast pairs](https://aigovernanceengineer.com/bok/ai-defined#contrast-pairs)
- Chapters: [ch. 11, AI Defined](https://aigovernanceengineer.com/bok/ai-defined) · [ch. 16, Fairness & XAI](https://aigovernanceengineer.com/bok/fairness-and-explainability)
- Contrast with: [Transparency](https://aigovernanceengineer.com/glossary/transparency) · [Interpretability](https://aigovernanceengineer.com/glossary/interpretability)
- In the glossary chapter: https://aigovernanceengineer.com/bok/glossary#t-explainability

## Commonly confused

- **Transparency** vs **Explainability**: What happened, from records of what ran, against how one decision was made Two artefacts: registry, cards and logs for the first; an explanation method with a fidelity test for the second

## 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)