Transparency, Explainability, and Security

This unit examines transparency and explainability as components of trustworthy AI, exploring their definitions, limits, and effects on stakeholders. It also covers technical and process-based approaches for explainability and introduces common AI compromises and exploitation vectors to prepare learners to design safer, more trustworthy systems.

Learning Objectives

• Analyze the nature and limits of transparency in AI systems and assess the impacts of transparency decisions on diverse stakeholders in realistic scenarios
• Evaluate technical and process-oriented explainability approaches by comparing their strengths, limitations, and suitability for specified use cases
• Apply leading practices and explainability techniques (e.g., model documentation, post-hoc explanations, and process controls) to design artifacts that improve stakeholder understanding and trust
• Demonstrate identification and assessment of AI security compromises by detecting common vulnerabilities, describing potential exploitation methods, and prioritizing mitigation steps