ISO/IEC 42001 Readiness

Objective
‍In today's rapidly evolving AI landscape, organizations must navigate complex challenges around AI governance, risk management, and compliance. This comprehensive AI Management System Specialist Certification Course provides essential training for professionals seeking to implement responsible AI practices, with a unique focus on both predictive and generative AI systems. 
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‍Through a balanced combination of classroom instruction, collaborative discussions, and personalized mentoring, participants will develop practical skills to create robust AI management frameworks that protect their organization's interests.
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By the end of the course, students will have learned how to create and manage an Artificial Intelligence (AI) Management System that is ISO/IEC 42001 certification-ready.

Daily Breakdown:

• 2 hours class time (lectures and case studies)
• 1.5 hours group discussion (problem-solving and collaborative analysis)
• 30 minutes one-on-one (personalized mentoring and project feedback)

Day 1: AI Literacy

• Class Time (2 hours):
  AI Literacy: Example of AI systems covering predictive and generative AI use cases
  Case Study: Examples of AI systems and their risk and benefit analysis
• Group Discussion (1.5 hours):
  Discuss AI Literacy concepts.
• One-on-One (30 mins):
  Discuss student's AI Management Systems example.
• Deliverable Focus:
  Begin the AI Management System Document example.
  By the end of Day 1, students will have a foundational understanding of AI systems, their applications, and associated risks. They'll also start developing critical thinking skills for evaluating AI implementations in various contexts.

Day 2: Quality Assurance for Predictive and Generative AI Systems

• Class Time (2 hours):
  AI Quality Assurance: Quantitative and qualitative evaluation methodologies and metrics
  Case Study: Evaluation of a predictive fraud detection model vs. generative text-to-image model
• Group Discussion (1.5 hours):
  Compare statistical evaluation methods for predictive and generative models.
• One-on-One (30 mins):
  Discuss how statistical methods will inform students' AI Management System example.
• Deliverable Focus:
  Add example model evaluation criteria to the AI Management System example.
  By the end of Day 2, students will understand the nuances of evaluating different types of AI systems, be able to choose appropriate metrics for various AI applications, and apply these concepts to real-world scenarios in their project work.

Day 3:  Security and Privacy in Predictive and Generative AI Systems

• Class Time (2 hours):
  Security in AI:
  Predictive AI: Risks like adversarial attacks
  Generative AI: Risks like data poisoning and hallucinations
  ‍Privacy in AI:
  Predictive AI: Handling sensitive data
  Generative AI: Addressing privacy concerns in large models
  Case Study: Security breaches in predictive vs. generative models
• Group Discussion (1.5 hours):
  Discuss the security and privacy risks for predictive and generative systems.
• One-on-One (30 mins):
  Review examples of security and privacy policies.
• Deliverable Focus:
  Outline sample security and privacy policies in the AI Management System example.
  By the end of Day 3, students will have a comprehensive understanding of the security and privacy challenges in both predictive and generative AI systems. They will be equipped to identify potential risks and develop appropriate policies to mitigate these concerns in real-world AI applications.

Day 4:  Ethics and Fairness in Predictive and Generative AI Systems

• Class Time (2 hours):
  Ethics in AI:
  Predictive AI: Ethical issues in decision-making
  Generative AI: Ethical concerns with content creation (e.g. deepfakes)
  Fairness:
  Predictive AI: Bias reduction strategies
  Generative AI: Ensuring fairness in AI-generated content
  Case Study: Bias in predictive vs. generative models.
• Group Discussion (1.5 hours):
  Debate ethical dilemmas and fairness issues in predictive vs. generative systems.
• One-on-One (30 mins):
  Discuss strategies for incorporating ethics and fairness in student’s AI Management System example.
• Deliverable Focus:
  Develop an example of AI Risk Management Policy, covering ethics and fairness.
  By the end of Day 4, students will have a deep understanding of the ethical considerations and fairness challenges in both predictive and generative AI systems. They will be equipped to develop policies and strategies that promote responsible and equitable AI development and deployment.

Day 5:  Societal and Operational Risks in Predictive and Generative AI Systems

• Class Time (2 hours):
  Societal Risks:
  Predictive AI: Job displacement due to automation
  Generative AI: Societal risks of misinformation
  Operational Risks:
  Predictive AI: Failure of predictive systems
  Generative AI: Operational scaling challenges
  Case Study: Societal and operational risks in predictive and generative systems.
• Group Discussion (1.5 hours):
  Analyze societal and operational risks for both AI types.
• One-on-One (30 mins):
  Provide feedback on the student's examples of risk management approaches.
• Deliverable Focus:
  Create an example of AI Risk Register and AI Risk Treatment Plan.
  By the end of Day 5, students will have a comprehensive understanding of the societal and operational risks associated with both predictive and generative AI systems. They will be equipped to identify, assess, and develop strategies to mitigate these risks in real-world AI applications.

Day 6: Legal and Compliance Risks in Predictive and Generative AI Systems

• Class Time (2 hours):
  Legal Risks:
  Predictive AI: Liability in AI decision-making systems
  Generative AI: Intellectual property issues
  Compliance in AI:
  Predictive AI: Global regulatory frameworks
  Generative AI: Legal challenges in generative content
  Case Study: Compliance issues in predictive and generative models.
• Group Discussion (1.5 hours):
  Discuss legal and compliance challenges unique to predictive vs. generative AI and appropriate AI incident responses.
• One-on-One (30 mins):
  Review examples of legal and compliance strategies as well as AI Incident Response plan in student’s AI Management System example.
• Deliverable Focus:
  Draft an example of an AI Audit Program Plan, including compliance measures and example of an AI Incident Response Plan.
  By the end of Day 6, students will have a thorough understanding of the legal and compliance risks associated with both predictive and generative AI systems. They will be equipped to develop strategies for navigating the complex regulatory landscape and implementing robust compliance and AI incident response measures for AI applications.

Final Deliverables
By the end of the course, students will submit the following:

• AI Management System Document: Example of an AIMS document covering both predictive and generative AI use cases.
• AI Risk Management Policy: Examples of ethical guidelines for both AI types.
• AI Risk Register: Examples of risks for both predictive and generative AI systems.
• AI Risk Treatment Plan: Examples of mitigation strategies for societal, operational, and legal risks.
• AI Audit Program Plan: Examples of audit strategies and compliance measures for both predictive and generative AI.
• AI Incident Response Plan: Examples of AI incident reporting and communication strategies.
• Roles and Responsibility Structure: Examples of key roles for managing both types of AI systems.

Assessment‍

• Participation in discussions and one-on-ones
• Submission and presentation of an example final AI Management System.

Fee per Participant: CAD $2,500
Certification Awarded: AI Management System Specialist Certificate