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AI Risk Management Course Outline

Day 1

Introduction to AI Risk and Ethics : [1 hour]

  • Overview of AI Risks: Discuss the various risks associated with AI, including bias, fairness, regulatory and privacy concerns.
  • Ethical Considerations: Highlight the importance of ethical AI design and the need for transparency and accountability.
  • Regulatory Landscape: Briefly introduce the current regulatory environment and its implications for AI adoption.

AI Ethics and Governance : [1.5 hours]

  • Ethical Principles: Discuss the principles of ethical AI use, including fairness, transparency, and human oversight.
  • AI Governance: Introduce frameworks for governing AI, such as the 6 consensus principles from the OECD Guidance on Ethics & Governance of Artificial Intelligence.

Exercise :  Given a Use Case, get participants to uncover aspects of the 6 principles

AI Risk Assessment and Mitigation [1.5 hours]

  • Risk Identification: Teach participants how to identify and assess AI-related risks.
  • Mitigation Strategies: Discuss various strategies for mitigating AI risks, including data quality, algorithmic transparency, and human oversight.

Exercise : Given different Use Cases, get participants to uncover aspects of risk

AI Regulation and Compliance [1.5 hours]

  • Regulatory Frameworks: Introduce key regulatory frameworks such as GDPR, CCPA, and the EU AI Act.
  • Compliance Requirements: Discuss the compliance requirements for AI, including data privacy and security.

Exercise : Given different Use Cases, get participants to uncover aspects of compliance

AI Use Case Analysis [1 hour]

  • Present examples of AI use cases in various industries, highlighting their benefits and risks.
  • Ethical Analysis: Teach participants how to analyse AI use cases from an ethical perspective.

Exercise : Case Studies: Discuss real-world examples of AI use cases and their ethical implications.

 

Day 2

AI Risk Management and Governance [1.5 hours]

  • Risk Management Strategies: Discuss strategies for managing AI risks, including risk assessment, mitigation, and monitoring.
  • Governance Frameworks: Introduce frameworks for governing AI, such as the IEEE Awareness Module on AI Ethics.

Exercise : Build an AI Governance Framework – tailored to the organization

AI Ethics and Responsible Use [1.5 hours]

  • Ethical Use of AI: Discuss the importance of ethical AI use and the need for responsible AI deployment.
  • Employee Training: Teach participants how to train employees on the ethical and responsible use of AI.

AI Implementation and Integration [1 hour]

  • Implementation Strategies: Discuss strategies for implementing AI in organizations, including data preparation, model selection, and deployment.
  • Integration Challenges: Highlight common challenges in integrating AI with existing systems and processes.

Exercise : Case Studies: Discuss real-world examples of AI use cases and their ethical implications.

AI Future Trends and Emerging Issues [1 hour]

  • Future Trends: Discuss emerging trends in AI, including generative AI and its ethical implications.
  • Emerging Issues: Highlight current issues in AI, such as AI bias, privacy concerns, and regulatory challenges.

Summary and Next Steps [1 hour]

  • Summary of Key Takeaways: Summarize the key points covered during the course.
  • Action Plan: Provide an action plan for participants to implement what they have learned.