The principle of Transparency and Explainability is designed to ensure that users and stakeholders can understand how AI systems function, how decisions are made, and what data is used.
ISO/IEC 42001:2023 emphasizes that transparency enables traceability, clarity of design choices, and auditability, while explainability provides insights into how outputs are generated, especially for high-risk or critical applications.
In practical terms, this principle supports:
Building trust in AI systems
Ensuring regulatory compliance
Facilitating informed decision-making
[Reference: ISO/IEC 42001:2023 – Clause 6.1.2 (AI risk identification), and 8.2.3 (Operational planning and control), PECB Lead Auditor Guide – Domain 1: “Transparency and Explainability” as a core ethical value of AI, ===========]
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