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Easy Learning with ISO 42001 Annex A Controls Explained
IT & Software > Network & Security
4.5 h
£14.99 £12.99
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Language: English

Mastering Responsible AI: A Practical Guide to ISO 42001 Annex A

What you will learn:

  • Gain a thorough understanding of each ISO 42001 Annex A control and its purpose.
  • Identify and mitigate real-world risks and governance challenges in AI systems.
  • Effectively map ISO 42001 controls to the AI lifecycle and compliance procedures.
  • Apply Annex A controls using practical checklists, templates, and real-world case studies.
  • Develop and implement a robust AI governance framework.
  • Conduct effective AI audits and gap assessments.
  • Ensure ethical and responsible AI development and deployment.
  • Improve organizational accountability and transparency in AI operations.
  • Mitigate bias and promote fairness in AI systems.
  • Build a culture of responsible AI within your organization.

Description

This course leverages artificial intelligence to provide a clear and practical understanding of the ISO/IEC 42001:2023 standard for AI Management Systems (AIMS). Led by Dr. Amar Massoud, a leading authority in the field, you'll navigate the complexities of Annex A controls with ease. Through real-world examples, insightful explanations, and downloadable checklists, this course empowers you to build robust, auditable, and ethically sound AI systems.

Gain a deep understanding of crucial aspects of AI governance, including fairness, bias mitigation, data quality, lifecycle management, stakeholder impact, transparency, and supplier responsibility. Prepare for audits, develop comprehensive AI compliance programs, and ensure organizational accountability. Each control is thoroughly dissected, enabling you to confidently implement and assess conformance. We utilize case studies featuring a model organization, InfoSure Ltd., to illustrate practical applications and best practices.

What this course offers that others don't:

  • Expert-led instruction combining AI-assisted learning with human expertise.
  • Detailed walkthrough of *every* Annex A control with clear explanations and practical applications.
  • Downloadable templates and checklists to streamline your AI governance processes.
  • Focus on real-world application through the InfoSure Ltd. case study.
  • Targeted for auditors, developers, compliance officers, and risk managers alike.

Whether you're a seasoned AI professional or just starting your journey in AI governance, this course will be an invaluable asset. You will be equipped with the knowledge and tools necessary to build and maintain ethically sound, auditable, and responsible AI systems. Enroll now and become a leader in responsible AI governance.

Curriculum

Introduction

This introductory section sets the stage for the course, beginning with a general overview of the course content. The lecture "Model Company – Synthovia HealthTech Ltd" introduces a fictional company used throughout the course to illustrate real-world applications of ISO 42001 Annex A controls.

AI Policies and Governance Structures

This section delves into the crucial aspects of establishing AI policies and governance structures. Lectures cover the creation and evaluation of AI policies (A.2.2), their alignment with other organizational policies (A.2.3), and the importance of regular policy reviews (A.2.4). It also explores defining roles and responsibilities (A.3.2) and establishing robust mechanisms for reporting concerns (A.3.3), using Synthovia HealthTech Ltd as a practical example for each control.

AI System Resources and Inventory

This section focuses on the management and documentation of AI system resources. Lectures cover the documentation of resources (A.4.2), data resources (A.4.3), tooling resources (A.4.4), computing resources (A.4.5), and human resources (A.4.6). The Synthovia case study helps illustrate effective resource management strategies to ensure compliance with ISO 42001.

Assessing the Impact of AI Systems

This section tackles the critical process of assessing the impact of AI systems. It details the processes for conducting AI system impact assessments (A.5.2), documenting these assessments (A.5.3), evaluating impacts on individuals and groups (A.5.4), and assessing potential societal impacts (A.5.5). Practical guidance and the Synthovia example support learning.

AI System Life Cycle Management

This section provides a detailed look at managing the entire AI system lifecycle. Lectures cover setting objectives for responsible development (A.6.1.2), processes for responsible design and development (A.6.1.3), defining AI system requirements (A.6.2.2), documenting design and development (A.6.2.3), verification and validation (A.6.2.4), deployment (A.6.2.5), operation and monitoring (A.6.2.6), technical documentation (A.6.2.7), and event logging (A.6.2.8). Synthovia provides context for practical application.

Data Governance for AI Systems

This section covers the essential aspects of data governance within the context of AI systems. It explains how to manage data for AI system development (A.7.2), ensuring ethical data acquisition (A.7.3), maintaining data quality (A.7.4), establishing data provenance (A.7.5), and the importance of proper data preparation (A.7.6), and how these apply to Synthovia.

Transparency and Communication with Stakeholders

This section focuses on transparency and communication. Lectures cover documenting system information for users (A.8.2), external reporting (A.8.3), incident communication (A.8.4), and information sharing with interested parties (A.8.5). The course shows how to maintain open communication using Synthovia as an example.

Responsible Use of AI Systems

This section explores the principles of responsible AI system use. Lectures address processes for responsible use (A.9.2), objectives for responsible use (A.9.3), and defining and documenting intended use (A.9.4). Synthovia is used to highlight practical applications.

Managing Third-Party and Customer Relationships

This section focuses on managing relationships with third parties and customers. It covers allocating responsibilities (A.10.2), managing suppliers (A.10.3), and engaging with customers (A.10.4) in the context of AI systems. Best practices and the Synthovia example provide valuable insights.

Conclusion

The concluding section summarizes the key concepts covered throughout the course and offers guidance for ongoing AI governance.