
Europe moved its hardest AI Act deadlines just weeks before they were due to apply. The public record shows which industry requests shaped the Digital Omnibus, which safeguards survived, and why the final result is more complicated than either “simplification” or “capture.”

The EU moved its hardest AI deadlines just weeks before they were due to apply. This paper traces which industry requests entered the Digital Omnibus, which were rejected, who benefits from the new timetable, and where the public record stops short of proving causation.

Most organizations can describe their AI governance. Far fewer can reconstruct what happened when an AI-enabled decision goes wrong. Paper IV of Project Sentinel introduces the Reconstruction Principle and a six-question test for determining whether governance can actually be demonstrated.

“The off switch was real. It just wasn’t in the building you thought it was.”

The full FairByDesign Doctrine Field Paper carries the complete three-pillar architecture, the five-level Evidence Hierarchy, the Reconstruction Spine and data-layer doctrine, role-by-role implications, Evidence Notes mapping every claim to its source, and full references. It is Paper III of the Project Sentinel series, following The Accountability Gap and The Accountability Control Plane. Download the…

Most organizations can prove they have an AI policy. Far fewer can prove what actually happened the last time an AI system shaped a real decision.

which governance functions can the protocol actually hold, and at exactly what point do you stop trusting it?

Trustworthy AI cannot be reduced to model behavior. It depends on trustworthy systems of oversight around AI.

FairByDesign — Operational AI Governance Series Field Signal Model Context Protocol, or MCP, is mostly discussed as an integration breakthrough. That is accurate, but incomplete. Anthropic introduced MCP on November 25, 2024 as an open standard for connecting AI applications to the systems where data lives — files, databases, APIs, developer tools, internal systems,…

AI governance is no longer just about accuracy. It’s about whether anyone stays accountable when AI shapes decisions — the accountability gap, and how to close it.