Training on AI Systems Architecture, Risk & Governance

Module 1: Foundations of AI Systems Architecture

• Core components of AI and machine learning systems including data pipelines, model training, deployment, and monitoring
• Principles of scalability, modularity, interoperability, and resilient AI system design
• Understanding how architecture decisions influence AI performance and reliability
• Case Study: Google AlphaGo success versus Google Photos mislabeling incident
• Practical: Map the architecture of an AI system and identify potential design weaknesses

Module 2: AI Risks: Technical, Ethical, and Operational Challenges

• Identifying bias, fairness, and data quality risks in AI systems
• Addressing adversarial attacks, model robustness, and cybersecurity concerns
• Managing operational risks such as model drift, reliability, and explainability
• Case Study: COMPAS algorithm bias and Microsoft Tay chatbot failures
• Practical: Conduct an AI risk identification and ethical assessment exercise

Module 3: Governance Frameworks for Responsible AI

• Overview of global AI governance frameworks including the EU AI Act, OECD Principles, and NIST AI RMF
• Developing organizational policies for AI accountability, transparency, and oversight
• Establishing governance structures involving ethics boards, audit trails, and risk committees
• Case Study: Facebook and Cambridge Analytica governance failures
• Practical: Design a governance framework for responsible AI implementation

Module 4: Risk Assessment and Compliance in AI Systems

• Applying AI risk management frameworks such as ISO/IEC 23894 and NIST guidelines
• Conducting AI risk and impact assessments
• Aligning AI systems with compliance, regulatory, and operational requirements
• Case Study: AI diagnostic systems in healthcare and regulatory compliance challenges
• Practical: Perform an AI risk and compliance assessment for a sample application

Module 5: Future Proofing AI Systems and Organizational Integration

• Embedding continuous monitoring, governance, and lifecycle management into AI systems
• Aligning AI governance with organizational strategy and ESG commitments
• Preparing for emerging risks including generative AI misuse, deepfakes, and AI in critical infrastructure
• Case Study: Governance approaches used in OpenAI GPT releases
• Practical: Capstone exercise: Develop an AI governance and risk management action plan