Foundations of AI and AI Governance

• What is AI? (Basics of ML, NLP, computer vision, etc.)

• Difference between AI, ML, and automation

• Why AI governance matters

• Risks and opportunities of AI

• Definitions: ethical AI, responsible AI, trustworthy AI

Principles of Responsible AI

• Core principles:

  • Fairness

  • Transparency

  • Accountability

  • Privacy

  • Safety and robustness

  • Human oversight

  • Aligning with company values or public standards (e.g., OECD, EU AI Act, ISO 42001)

AI Regulations and Compliance

• Overview of global AI regulations:

  • EU AI Act

  • GDPR (as it applies to automated decision-making)

  • NIST AI Risk Management Framework

  • ISO/IEC 42001 (AI management systems)

  • Industry-specific regulations (e.g., finance, healthcare, HR)

  • Obligations for high-risk and general-purpose AI systems

Governance Structures and Roles

• What is AI governance?

• Internal governance frameworks

• Role of:

  • AI ethics board

  • Data protection officer

  • AI product managers

  • Model risk management teams

  • Decision rights and escalation processes

AI Lifecycle Governance

• How to govern AI at each stage:

  • Data collection & labeling

  • Model design and development

  • Testing and validation

  • Deployment and integration

  • Monitoring and retirement

• Tools for governance:

  • Model cards

  • Datasheets for datasets

  • Audit trails

  • Version control and documentation

Risk Management and Impact Assessment

• How to identify, assess, and mitigate AI risks:

  • Bias and discrimination

  • Explainability gaps

  • Security vulnerabilities

  • Model drift

• Introduction to:

  • Algorithmic Impact Assessments (AIAs)

  • Data protection impact assessments (DPIAs)

  • Risk scoring and categorization (e.g., low-, medium-, high-risk AI)

Tools and Technologies for Responsible AI

• Fairness and bias detection tools (e.g., Fairlearn, Aequitas)

• Explainability tools (e.g., SHAP, LIME)

• Model monitoring platforms (e.g., Fiddler, WhyLabs)

• Governance platforms (e.g., Credo AI, Arthur AI)

Human Oversight and Escalation Paths

• Designing “human-in-the-loop” systems

• Decision override and review processes

• Red flags for escalation

• Incident response playbooks for AI errors

Communication and Transparency

• Explaining AI decisions to stakeholders

• Communicating AI limitations and risks

• Disclosures and user notices

• Engaging external stakeholders (e.g., regulators, customers)

Case Studies and Practical Exercises

• Real-world failures (e.g., biased recruiting AI, facial recognition misuse)

• Hands-on exercises:

  • Performing a model risk assessment

  • Designing an AI review checklist

  • Simulating an AI incident response

What you will learn

• Understand the basics of AI, ML, NLP, and computer vision.

• Differentiate between AI, ML, and automation.

• Gain knowledge of ethical, responsible, and trustworthy AI principles.

• Learn the core principles of Responsible AI: fairness, transparency, accountability, privacy, safety, robustness, and human oversight.

• Familiarize with global AI regulations and compliance frameworks (EU AI Act, GDPR, NIST AI RMF, ISO/IEC 42001).

• Understand industry-specific AI obligations (finance, healthcare, HR).

• Learn to design internal AI governance structures (ethics boards, DPO roles, escalation processes).

• Manage the AI lifecycle governance: data, model development, testing, deployment, monitoring, and retirement.

• Apply tools like model cards, datasheets, audit trails, and version control for AI governance.

• Conduct AI risk assessments (bias, explainability, security, model drift).

• Use AI fairness, bias detection, and explainability tools (e.g., SHAP, LIME, Fairlearn).

• Develop human-in-the-loop systems and escalation processes.

• Improve communication and transparency of AI decisions with stakeholders.

• Analyze real-world AI failures and apply lessons learned.

• Perform hands-on exercises: model risk assessments, AI review checklists, and incident response simulations.

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Ganesh Kannan(PMP) has more than 15 years of IT experience in Software testing, test Consulting, Project and Change management. He has worked for Investment Banks like Barclays Capital and IT services firm like Zensar Technologies. He has managed the testing tools and process function for a top tier investment bank and have managed large off-shore testing teams. He possess extensive project management and consulting experience in delivering IT applications and spearheads the classroom Fundamentals of software testing in Singapore.