Basil C. Puglisi

Artificial Intelligence (AI) & Digital Marketing Since 2009

Multi-AI Governance

Basil C. Puglisi's AI governance work across multiple AI platforms, where a named human holds binding authority at defined checkpoints in everything published.

AI Copyright Debate and the Inevitable Knowledge Transmission Argument for LLMs and Future AI

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Copyright protects expression, and the AI training cases now ask whether it also reaches what people and machines learn from it.

Basil C. Puglisi names the Inevitable Knowledge Transmission Argument and tests it against Bartz v. Anthropic, Thomson Reuters v. ROSS, GEMA v. OpenAI, and the 56 amicus briefs filed in the OpenAI litigation. The article separates acquisition, copying, training, memorization, and outputs, sets out two ways to pay creators without licensing learning, and records the strongest dissent against its own position.

A Call for an AI Disability Standard

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The protections exist, and the person has no way to invoke them that the next institution recognizes.

This third working paper in the AIDA series proposes a notice, written as a cease and desist, with a validation behind it built the way the parking placard is built. It maps where the notice has force today in the United States and the European Union, carries the full case against its own proposal, and puts 28 questions to the disability community.

NIST’s Ten Agent Identity Tenets: What Checkpoint-Based Governance Already Carried

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NIST published ten agent identity tenets on October 27, 2026. Nine were already in a public record that began in 2012.

This article lays the ten tenets against Checkpoint-Based Governance, the February 2026 GOPEL specification and code, and the Responsible AI desk that was publishing before the tenets appeared. Five match in full, four in part, and one, the agent’s own credential, is defined in the record and not yet issued. The reader gets the dates, the quotes, and the table to check them.

The Liability Counter-Pressure Reaches the Banks: AI Agent Risk Is Being Assigned to Consumers

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Agent providers and two major banks now assign an AI agent’s errors to the customer.

The article reads the Instinct terms, the Chase Digital Services Agreement, and Wells Fargo’s Section 15, which takes effect November 24, 2026, against the federal law that limits what such terms can settle. It sets Lead Bank’s proposed waterfall and the OCC’s November 4 examiner resource beside them, and names the five records that will decide where the first contested loss stays.

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.

If You’re So Intelligent, Why Do You Use AI?

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The question every AI user hears at the table, answered with a story.

Teresa drafts grant proposals with a machine. Her brother-in-law Dale has never typed a prompt and asks why someone intelligent would. Her answer runs through Tron, Jurassic Park, and two versions of The Shining, and lands on the three old reasons people refuse a new tool: jealousy, fear, and prejudice. Verified history, invented people, one backyard argument.

Physical AI Is the Next Platform Shift, and Chat Was Only the On-Ramp

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Chat made AI legible to every board, and robots that see, plan, and act now make it consequential.

Frontier capital now treats robot learning, autonomous science, and new interfaces as the next compounding domains beyond language and code. Tesla’s Optimus and Robotaxi record shows how far demonstrations still sit from deployment, so this analysis sets out how to fund physical AI pilots with preregistered measures, a classical baseline, engineered safety, an operating-mode ledger, and a named human who decides.

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.