Ethical AI Governance and Compliance #254012

Upon completion of this course, participants will be able to:

• Articulate Core Principles: Define and explain foundational ethical AI principles (e.g., fairness, accountability, transparency, privacy) and major governance frameworks (e.g., NIST AI RMF, EU AI Act, OECD principles).

• Navigate the Regulatory Landscape: Identify and interpret key AI regulations and standards across major jurisdictions, including the EU AI Act, the US Executive Order on AI, and sector-specific guidelines.

• Implement Governance Structures: Design and implement a practical AI Governance program within an organization, including roles, responsibilities, policies, and controls.

• Conduct Risk Assessments: Apply methodologies like impact assessments and conformity assessments to evaluate and mitigate the risks of AI systems throughout their lifecycle.

• Ensure Transparency and Accountability: Develop strategies for documentation (e.g., AI Bill of Materials), explainability, and establishing clear lines of accountability for AI systems.

• Audit and Monitor AI Systems: Understand the components of an AI audit and establish processes for continuous monitoring and compliance verification.

This course is essential for professionals involved in the development, deployment, oversight, and regulation of AI systems:

• AI Governance & Ethics Officers: New and existing roles dedicated to overseeing ethical AI practices.

• Compliance, Risk, & Legal Professionals: Chief Compliance Officers, legal counsel, and risk managers navigating the new regulatory environment.

• Data Scientists & ML Engineers: Builders of AI systems who need to integrate ethical design and compliance requirements into their workflows.

• Technology & Product Leaders: CIOs, CTOs, CPOs, and product managers responsible for AI strategy and product launches.

• Information Security & Privacy Officers: Professionals (e.g., CISOs, DPOs) integrating AI governance with existing security and privacy programs.

• Corporate Executives & Board Members: Decision-makers who need to understand their oversight responsibilities and liability concerning AI.

• Internal & External Auditors: Auditors expanding their expertise to cover AI systems and algorithms.

• Policy Makers & Regulatory Affairs Specialists: Government officials and consultants shaping and interpreting AI regulation.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of Ethical AI

• AM: The Why: From Principles to Practice

  • The Business and Societal Case for Ethical AI.

  • Core Ethical Principles: Fairness, Accountability, Transparency, Explainability (FATE), Privacy, and Safety.

  • Overview of Global Frameworks: OECD AI Principles, UNESCO Recommendations, and industry-specific guidelines.

• PM: The Language of AI Governance

  • Key Concepts: AI, ML, Neural Networks, Generative AI.

  • The AI System Lifecycle: From design and data collection to deployment, monitoring, and decommissioning.

  • Workshop: Identifying ethical “red flags” in a real-world AI use case.

Day 2: The Regulatory Landscape

• AM: Decoding the EU AI Act

  • In-depth analysis: The risk-based approach (Prohibited, High-Risk, Limited Risk, Minimal Risk).

  • Requirements for High-Risk AI Systems: Conformity assessments, data governance, technical documentation, and human oversight.

  • Obligations for General Purpose AI (GPAI) and Generative AI.

• PM: A Global Patchwork of Regulations

  • US Approach: Executive Order on AI, NIST AI RMF, and state-level laws (e.g., Colorado AI Act).

  • Other Key Jurisdictions: China, Canada, the UK, and Singapore.

  • Sector-Specific Rules: Financial services (SEC, FINRA), healthcare (FDA), and automotive.

  • Group Exercise: Categorizing AI systems by risk level under different regulatory regimes.

Day 3: Building an AI Governance Program

• AM: The Pillars of Governance

  • Roles & Responsibilities: Establishing an AI Governance Board, defining roles of AI Ethics Officers, product teams, and legal.

  • Core Policies & Standards: Developing internal AI policies, model development standards, and procurement guidelines.

  • The AI Inventory: Creating a registry of AI systems and their risk classifications.

• PM: Risk Management in Practice

  • Applying the NIST AI Risk Management Framework (RMF).

  • Conducting AI Impact Assessments (AIIA) and Fundamental Rights Assessments.

  • Workshop: Drafting an AI Impact Assessment for a proposed project.

Day 4: Operationalizing Ethics: Tools and Processes

• AM: Ensuring Technical Compliance

  • Bias Detection and Mitigation: Identifying metrics and techniques for testing and ensuring fairness.

  • Explainability (XAI) Methods: Tools for making model outputs understandable to users and auditors.

  • Data Provenance and Documentation: Creating AI Bills of Materials (AI BOM) and model cards.

• PM: Human Oversight and Accountability

  • Designing Effective Human-in-the-Loop Processes.

  • Establishing Clear Lines of Accountability and incident response protocols.

  • Case Study: Analyzing a past AI failure and identifying governance breakdowns.

Day 5: Auditing, Monitoring, and Future-Proofing

• AM: The AI Audit

  • Internal vs. External Audits: Scoping and preparing for an AI compliance audit.

  • Audit Checklists: Verifying documentation, testing for bias, and assessing data practices.

  • Continuous Monitoring: Tracking model performance, drift, and ongoing compliance post-deployment.

• PM: Capstone and Strategy Session

  • Capstone Exercise: Teams are given a scenario to develop a full governance plan for a new AI application, covering risk classification, policies, controls, and monitoring.

  • Presenting Governance Plans and Peer Feedback.

  • Course Wrap-Up: Future Trends in AI Regulation and Building a Sustainable Governance Culture.