Basil C. Puglisi

Artificial Intelligence (AI) & Digital Marketing Since 2009

human oversight

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.

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.

Humans Are Optional When Defining AI Governance for ISO, According to the U.S. Technical Advisory Group

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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

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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.

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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.

Measuring the Governance Competence at the Center of AI Literacy

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The major AI literacy frameworks name governing and managing AI as a competence and stop short of measuring it. This working paper introduces the Human Enhancement Quotient, a behavior-anchored instrument that scores the governance relationship between a human authority and a machine capability inside a working decision process, with a universal floor keyed to irreversible consequence and an audit record reviewable on appeal.

CORE: Content Optimization Reader Evaluation, and How to Score an Article Before Anyone Reads It

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Most content review checks the wrapper. Content Optimization Reader Evaluation checks the argument, the evidence, and whether anyone directed the prose.

A rubric that scores an article on substance is only useful if it stops when something is wrong. CORE scores six pillars, holds the run when a pillar fails or a source does not support its claim, and hands the decision to a person. The full prompt is included.

HAIA-CAIPR: Cross AI Platform Review

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Nine judges carry two votes’ worth of information. Run the same task across many AI platforms and the agreement that comes back may be a shared blind spot rather than a finding. HAIA-CAIPR is the governance framework for orchestrating that comparison so it produces evidence instead of confidence. It sets nine invariants, five configuration variables that each name what they cost, and a named human who reads the raw returns before anyone decides.