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The AI Governance Textbook Episodes 9-17 Now Available

A Paradigm Shift from Performance Evaluation to Predefining Deployment Conditions

As shown in the latter half (Episodes 9-17) of the video series "The AI Governance Textbook" currently available on YouTube, the essence of AI governance lies not in evaluating whether AI is "smart," but in "predefining conditions that enable real-world deployment." We explain the transition from the era of performance evaluation to the phase of designing responsibility and operational conditions.

▼ Presented by: AI Accountability Project https://www.ghostdriftresearch.com/ai-accountability-project



What you will learn in this series

  • The structural difference between accident prevention (risk management) and fixing the basis for decisions (accountability).

  • The clear line between "being understandable" (explainability) and "being verifiable by a third party" (accountability).

  • The "minimum structure of responsibility" capable of withstanding audits, which regulations and standardization demand in practice.


Target Audience

Executives responsible for decision-making regarding AI deployment, as well as engineers, researchers, and legal professionals responsible for designing actual operations and audits. For those seeking not accuracy improvement, but specific requirements to assume responsibility as an organization and break through the barriers to real-world deployment.


"The AI Governance Textbook" Latter Half (Episodes 9-17) List

You can watch each episode from the links below:


Episode 9: What is AI Risk Management?

Explains the structural difference between risk management and accountability, and the necessity of a verifiable evidence structure.


Episode 10: What is an AI Audit?

Explains the real reasons why AI fails audits and the minimum structure (Commit / Ledger / Verify) required for responsibility to remain.


Episode 11: What is AI Management?

Explains why AI deployment stops at the management level and the fixed responsibility structure for an organization to take responsibility for decisions.


Episode 12: What is an AI Verification Tool?

Explains why verification (evaluation) alone cannot lock in responsibility, and the minimum design to establish it. 🎥 https://youtu.be/1RmTuW5IlPY?si=sRt6QEXuooOSrEZm


Episode 13: What is an AI Accountability Tool?

Going beyond XAI, it explains the minimum structure for establishing responsibility (pre-definition of boundaries / fixing evidence / third-party verifiability).


Episode 14: What is the Difference Between AI Accountability and Explainability?

Clarifies the difference between understanding and verification, explaining the perspective that separates explanation and responsibility.

Episode 15: What is the EU AI Act?

Explains the shift to an era where AI is evaluated not by "performance" but by "deployment conditions," and the essence of accountability.


Episode 16: What is the Difference Between AI Law and AI-Related Laws?

Explains the related laws that directly and jointly constrain practical operations, and the fixing of operational conditions necessary to prevent responsibility from evaporating. 🎥 https://youtu.be/aCLQl_fn7yY?si=uAlHHdSu-s7wk_M2


Episode 17: What is AI Standardization from Japan?

Rather than ideals, it explains the five requirements necessary for implementation candidates usable in audits, and the integrated responsibility architecture.


 
 
 

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