Safety, Alignment & Ethics

Explainability

Making AI decisions understandable to non-technical users.

Definition

Explainability focuses on providing human-understandable explanations for AI outputs — why did the model flag this transaction as suspicious? Why did it recommend this candidate? Explainability is distinct from interpretability (which looks inside the model's mechanics) — it focuses on producing explanations that are useful to the people making decisions. It is a regulatory requirement in many sectors and is important for building user trust.

Related Terms

Interpretability

The ability to examine what is happening inside a model to understand its reasoning.

Transparency

The ability to understand and explain how an AI reaches its outputs.

Responsible AI

A framework for developing and deploying AI in ways that are ethical and accountable.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Safety, Alignment & Ethics·Explainability