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Foundations of Ethical and Trustworthy AI

Module 1: What ethical and trustworthy AI mean

Trustworthiness

Bridging Principles to ISO/IEC 42001 Controls

Module 2: Turning principles into practical 42001 controls

AI Risk Assessment

AI System Lifecycle Management

Pixel Quest

Auditing and Evidencing Trustworthy AI

Module 3: Evidence, audit and continual improvement

Internal Audit

Quiz

Quiz 1 - AI transparency

Quiz 2 - AI Risk Treatment and Control Measures

Quiz 3 - AI Objectives and Planning to Achieve Them

Quiz 4 - Documented Information

Quiz 5 - Nonconformity and Corrective Action

Survey/Feedback

Feedback

Badge Card

Welcome to "Translating Trustworthy AI Principles into ISO/IEC 42001 Operational Controls. " As artificial intelligence continues to reshape industries, the demand for ethical, transparent, and accountable AI systems has never been higher.

$ (USD)

Welcome to "Translating Trustworthy AI Principles into ISO/IEC 42001 Operational Controls." As artificial intelligence continues to reshape industries, the demand for ethical, transparent, and accountable AI systems has never been higher. However, bridging the gap between abstract ethical principles and actionable operational controls remains a significant challenge for many organizations. This comprehensive course is designed to demystify that process, guiding you through the integration of trustworthy AI concepts into the robust framework of ISO/IEC 42001.

Throughout this course, you will explore the foundational elements of ethical AI, including fairness, transparency, and accountability, and learn how to map these concepts directly to ISO/IEC 42001 and ISO/IEC 22989 standards. We will dive deep into practical applications, showing you how to conduct effective AI risk assessments, manage the AI system lifecycle, and implement rigorous operational controls. Furthermore, you will master the art of auditing and evidencing trustworthy AI, ensuring your organization not only talks the talk but walks the walk with audit-ready compliance and continual improvement strategies.

Whether you are a governance professional, an AI project manager, or a compliance officer, this course will equip you with the practical tools and knowledge needed to transform high-level AI principles into measurable, auditable, and effective operational realities. Join us to lead the charge in responsible AI deployment and safeguard your organization's AI initiatives against emerging risks.

ISO/IEC 42001 - Turning Principles into ISO/IEC 42001 Controls

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            <p>You have probably heard the high-level buzzwords surrounding artificial intelligence—fairness, transparency, accountability, and responsible use. But as ISO-literate governance, risk, and assurance professionals, you know that abstract ethical AI principles do not hold up in an audit unless they are tied to concrete operational controls. This course bridges that exact gap. We are going to take the philosophical concepts of trustworthy AI and translate them directly into the actionable, auditable language of ISO/IEC 42001. If you are tasked with integrating AI into your existing management systems or overseeing AI compliance, you need more than just a corporate policy; you need a clear path to risk assessment, lifecycle management, and solid, verifiable evidence.</p>

<p>Throughout this journey, we will start by grounding ourselves in the foundations of ethical AI, using ISO/IEC 22989 to bring clarity and consistency to the terminology so we are all speaking the same governance language. From there, we will roll up our sleeves and map these broad principles directly to practical ISO/IEC 42001 controls. You will learn how to apply robust AI risk assessments to evaluate potential failures in fairness or transparency, and how to embed trustworthiness right into the AI system lifecycle. Finally, because a control is only as good as the evidence proving it works, we will dive deep into auditing. We will cover how to ask the tough implementation questions, design effective internal audit plans, and gather the audit-ready evidence necessary to prove your organization's AI principles are actually embedded in daily practice and driving continual improvement.</p>
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