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Chapter 03. Learning Objectives

  • Explain how AI risk management frameworks enable transparency and technical accountability throughout the system lifecycle.
  • Analyze the consequences of ignoring technical safety in high-performing systems, using real-world failures.
  • Evaluate the effectiveness of oversight mechanisms and fallback protocols as part of safety infrastructure.
  • Compare symbolic versus structural human-in-the-loop designs in AI safety and risk response systems.
  • Apply ISO 31000, ISO/IEC 23894, ISO/IEC 42001, and NIST RMF standards to risk lifecycle stages.
  • Examine how organizations can integrate technical safety into deployment pipelines and post-market updates.
  • Assess how risk-based frameworks translate ethical design goals into operational safety mechanisms.