Basil C. Puglisi

Artificial Intelligence (AI) & Digital Marketing Since 2009

Checkpoint-Based Governance

Agents or Agency: Transparency Is Success

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Automation and human checkpoints each have a place and a rule, and the failure is selling one as the other.

Basil C. Puglisi frames Responsible AI as the factory, where the machine checks the machine, and AI Governance as the handmade product, where a named human holds binding authority at checkpoints. Drawing on enforcement actions, court cases, and research on AI labels, the article sets out where each model belongs, what transparency costs, and how European rules, SEC enforcement, Colorado’s new law, and New York’s court rule reach the same line by different routes. It proposes an inspection mark that shows the customer who answers.

HAIA Agents: From the Definition Problem to Running Code

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The definition problem of January 2026 now runs as code.

This rebuild of the HAIA-RECCLIN Agent Architecture Specification introduces the Alpha release of Agent Model 1, Responsible AI written as code that passes nothing without a gate, and Agent Model 2, the hybrid that runs through to AI Governance at a signed human checkpoint after every role. It revisits Agent Model 3, Manual Human AI Governance, which built both releases, and states plainly what the Alpha does not claim.

Narrative Is Not Custody: Why AI Compliance Needs Traversable Rules

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A fluent explanation is not a record, and regulated AI decisions need a record.

This article argues that AI compliance starts with accountability, with policy graphs and solvers as one route to a traversable trail. It reports what GraphCompliance, solver-backed legal analysis, and two recent preprints find, maps the GDPR, EU AI Act, and Regulation B duties, and sets out the two human checkpoints that turn a trail into AI Governance.

What Would Education Look Like With an AI Literacy Program Based on Proven Methods?

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Education already owns the methods that make AI support learning.

This is what an AI literacy program built on them would look like, with a named human at every checkpoint. It opens with the ground rules before any assignment uses AI, runs every session through one checkpoint sequence, and measures each learner with the tools removed. Three programs carry it from a high school classroom to a professional certificate.

Superintelligence Has Authors, the US Government Is Not One of Them

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President Trump renamed artificial intelligence after a mind no one has built.

Executive Order 14434 gives today’s ordinary AI the name researchers reserved for an intellect beyond ours, and the minds who defined that word were not in the room. Basil C. Puglisi traces superintelligence from Turing and Good to Bostrom and shows how the order skips the AGI rung. The rename is a bad joke if superintelligence is far off and dangerous if the fears are right.

Human-AI Collaboration Needs More Than a Human in the Loop

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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.

A Year Ago I Told You AGI Was Hype. Superintelligence Is Further Off Than Sold, and It Will Not End Us.

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The machine is never the one who answers for what it does.

A September 2025 essay argued that AGI talk is usually about money and not reality. This September, Andrew Ng, Timnit Gebru, and Emily Bender reached the same point about responsibility from opposite sides of the AI debate. The piece separates real capability from the claims stacked on it, argues that superintelligence is further off than sold and will not end us, and moves from a human chain of responsibility to a real checkpoint.

When AI Policy Becomes an Accessibility Barrier

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If AI is a disability tool, are the rules restricting it unethical at best and illegal at worst?

This companion working paper maps which United States laws reach which institutions and classifies six types of AI policy risk, from outright bans to detection tools. It works a pending smart-glasses case, compares the EU AI Act, and closes with a model accommodation clause any institution can adopt.

When AI Becomes Assistive Technology

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A blind professional asks AI to describe a chart. The paper asks whether that use is assistive technology.

This working paper names the function Artificially Intelligent Disability Assistance (AIDA) and gives a three-prong test for when a use of general-purpose AI qualifies. It runs seven cases, from blindness and speech loss to post-traumatic stress, and it names the case the test excludes and the dissent it has to answer.

What Is “Verified AI”?

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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.