Glossary

AI Transparency

What is AI Transparency?

AI transparency means disclosing when AI is involved, which data and sources its results are based on, and how decisions are reached. This includes labelling AI content, traceable source references, logs and documented changes. Transparency builds trust, makes troubleshooting easier and is partly required by law, for example by the EU AI Act's transparency obligations, under which people should be able to tell that they are interacting with an AI.

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Topics

Transparency
Trust
Traceability

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Related terms

Algorithmic transparency

Algorithmic transparency means it is possible to understand how an automated system reaches a result: which data it uses, which rules it follows and where its limits lie. This builds trust, makes errors easier to trace and is increasingly a legal requirement, for example through information duties under the GDPR and the EU AI Act. In practice, documentation, source citations, logs and clear notices for affected people help.

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Explainability

Explainability (explainable AI, XAI) describes how well one can understand why an AI system reached a particular result. It covers methods that show which inputs influenced a decision most, as well as practical aids such as source references and logs. Explainability matters for trust, troubleshooting and regulation – for example when a bank has to justify why an application was declined.

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Model interpretability

Model interpretability describes how well people can understand how an AI model arrives at its results. Simple models such as decision trees are inherently easy to read; for complex neural networks, techniques that reveal the influence of individual features can help. Interpretability matters for debugging, trust and regulatory requirements, for example when a loan rejection has to be justified.

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AI Ethics

AI ethics deals with the moral questions surrounding the development and use of AI: fairness and discrimination, transparency and explainability, data protection, responsibility for errors, safety and the effects on work and society. For organisations it becomes concrete in guidelines, risk assessments, human oversight and honest communication about AI's limitations. Example: before a CV screening tool is introduced, it is checked for whether it disadvantages particular groups.

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