32 articles in the AI Governance track of the Deep Trust Governance Series.
Every AI governance framework that addresses accountability requires traceability: the ability to follow a decision back through the system that made it, to the data that…
Every AI governance framework that addresses bias requires that it be detected and mitigated. What no framework specifies is which measurement methodology constitutes ade…
The quality of an AI model's output is bounded by the quality of the data it learned from. Organizations that deploy AI systems without verifying the quality of their tra…
Human oversight was designed to review AI outputs before they produce consequences. Automated workflows execute AI outputs immediately, at machine speed, connecting AI re…
Human oversight of AI systems is required by virtually every AI governance framework. Organizations implement it through review queues, exception processes, and human-in-…
Regulators require that humans remain in control of consequential AI decisions. Operational reality requires that AI systems process thousands of decisions per hour.
The output of an AI system is what the system produced. The decision logic is why it produced it. These are not the same thing. Governance that evaluates outputs without…
The EU AI Act does not classify AI systems by what organizations intend them to do. It classifies them by what they actually do, and by the context in which they operate.
