Safety, Alignment & Ethics
Transparency
The ability to understand and explain how an AI reaches its outputs.
Definition
Transparency in AI means being able to explain what data a model was trained on, how it makes decisions, and why it produces particular outputs. This is increasingly required by regulation (particularly under the EU AI Act and UK AI legislation) for high-risk applications. It is also important for organisational trust — stakeholders are more likely to trust and correctly use AI outputs when they can understand the basis for them.
Related Terms
Explainability
Making AI decisions understandable to non-technical users.
Interpretability
The ability to examine what is happening inside a model to understand its reasoning.
Responsible AI
A framework for developing and deploying AI in ways that are ethical and accountable.
Model Card
A document that describes a model's capabilities, limitations, and intended uses.
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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·Transparency