Training on AI Systems Architecture, Risk & Governance

Module 1: Foundations of AI Systems Architecture

• Key components of AI/ML systems: data pipelines, training, deployment, and monitoring

• Scalability, modularity, and interoperability in AI system design

• Case study: Google’s AlphaGo vs. Google Photos mislabeling incident—contrasting high-performance architecture with real-world design flaws

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

• Understanding bias, fairness, and data quality risks

• Addressing adversarial attacks and model robustness

• Identifying operational risks in AI lifecycle (drift, reliability, and explainability)

• Case study: COMPAS recidivism algorithm bias and Microsoft Tay chatbot—failures from technical and ethical blind spots

Module 3: Governance Frameworks for Responsible AI

• Overview of global AI governance standards: EU AI Act, OECD Principles, NIST AI RMF

• Building organizational policies for AI accountability and oversight

• Governance models integrating ethics boards, risk committees, and audit trails

• Case study: Facebook/Cambridge Analytica scandal—how lack of governance fueled misuse of AI-driven systems

Module 4: Risk Assessment and Compliance in AI Systems

• Applying risk management frameworks to AI: ISO/IEC 23894, NIST

• Conducting AI risk and impact assessments (AIRA)

• Mapping risks to compliance and regulatory requirements

• Case study: Healthcare AI diagnostic tools—balancing innovation with safety and regulatory compliance

Module 5: Future-Proofing AI Systems and Organizational Integration

• Embedding continuous monitoring and governance into AI lifecycle

• Integrating AI governance with corporate strategy and ESG commitments

• Preparing for emerging risks: generative AI misuse, deepfakes, and AI in critical infrastructure

• Case study: OpenAI GPT releases—governance approaches to balance innovation with societal impact