Unit 6
Transparent AI
Transparency turns AI systems into inspectable operations: people can see what data was used, how decisions were made, who is responsible, and what evidence exists when something goes wrong. This unit covers traceability, provenance, auditability, failure analysis, and transparency patterns for foundation models, RAG, agents, and multi-agent systems.
Chapter 1
Transparency
Transparency is the work of making an AI system inspectable before anyone asks for an explanation. This chapter builds the basic evidence layer: traceability, provenance, data transparency, and documentation.
Chapter 2
Accountability
Accountability connects transparency to responsibility. You will learn how audits, monitors, logs, and incident response plans turn evidence into action when an AI system affects people.
Chapter 3
Failure Analysis
Transparent systems make failures easier to find and explain. This chapter focuses on dataset bias, shortcut learning, spurious correlations, and distribution shift as practical failure modes.
Chapter 4
Transparency in Modern AI Systems
Modern AI systems are pipelines, products, and organizations, not just model files. This chapter applies transparency to retrieval-augmented generation, agentic systems, and multi-agent workflows.