Bennet/Welch Federal Digital Commission and Warner/Schatz AI Safety Board landed within 48 hours. Operators must map pre-clearance/pause vs Commerce standards and Model Safety Plans.
负责任的人工智能
Autonomy Is Not a Defense: AI Agent Hacks Meet California Liability Law #AIg
LASST sued OpenAI over AI agent access to Hugging Face as California bars the autonomy defense. September also brought British Columbia failure-to-warn claims and a Florida AG injunction motion, while CGL carriers carve generative AI out of standard coverage.
Trump Renames Federal AI Language to Super Intelligence #AIg
Federal SI branding meets statutory AI. The September 29 Executive Order remaps executive-branch vocabulary to Super Intelligence without rewriting statute, and the collision with technical superintelligence is the governance risk operators must manage.
Human-AI Collaboration Needs More Than a Human in the Loop
Human oversight of automated systems is older than AI, and current AI practice still falls short of it.
This paper traces human oversight and accountability from the cybernetics literature of 1948 to Article 14 of the EU AI Act, then separates Responsible AI from AI Governance. It sets out Checkpoint-Based Governance, where a named human holds binding authority to accept, modify, or reject an AI output and leaves a record, and it proposes the studies that would test it. It closes on the Growth OS, a future of work in which AI amplifies people instead of replacing them.
What Is “Verified AI”?
Verified AI is not a settled category, and its history explains why.
This working paper traces the term from the verification of knowledge-based systems in 1988 through the 2016 formal definition, shows how that concept shaped the HAIA Ecosystem, and sets out a practitioner definition built on records a human can review and accept. It closes with the 2026 trend and how the Proof-of-Control standard contrasts with it.
Humans Are Optional When Defining AI Governance for ISO, According to the U.S. Technical Advisory Group
A standards body just wrote down that AI governance can exist without a human in control.
In September 2026 the U.S. TAG to ISO/TC 258 answered a public comment on the draft AI standard for project management. It accepted two editorial corrections and rejected the one that would have put binding human authority and attributable accountability inside the definition. This is the filing, the four reasons for the rejection, and why a definition without a governor fails the moral test the public is already applying.
What AI Transformation Actually Requires
AI products can be purchased. AI transformation has to be built.
This paper reframes fifteen commonly cited requirements for AI transformation as operational management requirements, from data provenance and purpose to decision traceability and value realization. It shows why automation and observation cannot serve as the sole control on consequential work, and how named human checkpoints close that gap.
AI Was Never New. It Just Started Talking to Us Directly.
A woman applies for a car loan on a Tuesday morning. Before a human being reads her name, a model has scored her, a ranking system has chosen what she sees, and a classifier has flagged her scan.
She met artificial intelligence roughly a dozen times before lunch, and none of it said a word to her. This is the story of what AI actually is, told in the order it happened, from the era when humans wrote every rule through machine learning, deep learning, the transformer, language models, agents, and embodiment. It ends where the sequence was never resolved: nobody settled who holds authority over the decisions.
Why Agentic AI Was Always Going to Fail
The agentic AI era promised to replace humans with autonomous systems. The evidence shows it failed on two fronts: the technology cannot reliably do what it promised, and the public is rejecting the premise even where it partially works. This paper introduces the Named-Human Test, a single sorting question that separates what failed from what survives, and traces that line across production benchmarks, supermajority polling, enacted law, and frontier-lab disclosures.
The AI Risk Economy: Why Insurance Cannot Price What Governance Cannot Prove
Insurance carriers are writing the rules of AI governance before legislators finish debating them. This working paper proposes a five-tier model that maps where organizations fall on the spectrum from excluded to insurable, identifies the actuarial gap at the center of the emerging practice, and documents the carrier evidence, regulatory signals, and market products that are forcing the distinction between governed and ungoverned AI into the open.