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SESK: Stuff Everyone Should Know · sesk.info/ai-explainability-vs-interpretability

How does NIST distinguish explainability from interpretability?

Explainability describes how; interpretability gives output meaning in context.

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  1. Mechanism versus contextual meaning ✔
  2. Training versus deployment speed
  3. Accuracy versus precision

Why: Explainability represents mechanisms behind operation or a decision. Interpretability connects an output to meaning in the user's situation, answering a different question.

Huh, didn't know that: NIST separately describes transparency as answering what happened in a system.

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The model explained how, and the user interpreted why.
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References

Answer: Mechanism versus contextual meaning. Explainability represents mechanisms behind operation or a decision. Interpretability connects an output to meaning in the user's situation, answering a different question.

  1. NIST AI Risk Management Framework 1.0 nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
    Explainability refers to a representation of the mechanisms underlying AI systems' operation
    Checked 2026-10-10.

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