Chapter 02. Trustworthy Frameworks for AI Governance¶
Every AI system makes decisions that shape real lives, but when those decisions go wrong, the question becomes: Who is accountable? Accountability is not a vague moral obligation. It is a structured system of control, oversight, and consequence that must be embedded from design to deployment.
In this chapter, we explore how accountability is governed at multiple levels, using a three-tiered governance structure:
System Governance, Organizational Governance, and Technical Governance, each layer supported or challenged by Legal and Societal Oversight.
This framework helps us trace how responsibility moves across actors and across time: from global policy bodies to AI developers, from corporate ethics boards to the fallback systems coded into models. And most importantly, it shows where that responsibility often breaks down.
We align each governance layer with real-world practices and failures:
- System Governance (2.1): Institutions that define the boundaries of AI power, governments, regulatory bodies, and standard-setting organizations
- Organizational Governance (2.2): Internal structures that shape how AI is developed, reviewed, and released, risk officers, compliance teams, executive mandates
- Technical Governance (2.3): Design-level mechanisms that enforce accountability, fallback logic, explainability, traceability, and oversight features
- Legal & Societal Oversight (2.4): External mechanisms of justice and redress, regulators, courts, journalists, and the public
These layers are not isolated, they are interconnected. For example, an accountability gap in model design (technical) may stem from the absence of ethical leadership (organizational), which may in turn reflect unclear mandates from public regulators (system-level).
By understanding how these governance layers interact, and where they fail, we begin to see why building trustworthy AI is not just about better models, but about better systems of responsibility.
In the Advanced Level, we focus on how to operationalize governance through auditing frameworks, accountability metrics, and automated compliance pipelines.