Explainable AI (XAI)

AI systems whose decision-making is transparent and comprehensible to users, required under ISO 42001.

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Explainable AI (XAI) refers to artificial intelligence systems and techniques designed to make AI decision-making processes transparent, interpretable and understandable to human users. Under ISO 42001 and the EU AI Act, organisations deploying AI systems must be able to explain how decisions are made, what data influences outcomes and why a particular result was produced, especially in high-risk applications such as credit scoring, hiring and medical diagnosis.

Implementing explainable AI is not just a regulatory requirement but also a business imperative, as organisations that cannot explain their AI decisions face reputational risk, legal liability and erosion of stakeholder trust. Techniques such as SHAP values, LIME, attention visualisation and decision trees help make complex models more interpretable, enabling organisations to satisfy audit requirements, identify and address algorithmic bias and maintain meaningful human oversight of automated decision-making.

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Frequently asked questions

What is Explainable AI (XAI)?
Explainable AI (XAI) refers to AI systems whose decisions can be understood and explained to people, rather than operating as an opaque black box.
Why does Explainable AI matter?
Explainability builds trust, supports accountability, and helps meet requirements under standards such as ISO 42001 and the EU AI Act.
How is Explainable AI achieved?
Through techniques and documentation that make a model’s inputs, logic and outputs transparent and interpretable to users and auditors.
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