Thought Leadership

The Real AI Threat Is Not the Algorithm. It’s That No One Answers for the Decision.

October 18, 2025 by Basil Puglisi Leave a Comment

When Detective Danny Reagan says, “The tech is just a tool. If you add that tool to lousy police work, you get lousy results. But if you add it to quality police work, you can save that one life we’re talking about,” he is describing something more fundamental than good policing. He is describing the one difference that separates human decisions from algorithmic ones.

When a human detective makes a mistake, you know who to hold accountable. You can ask why they made that choice. You can review their reasoning. You can examine what alternatives they considered and why they rejected them. You can discipline them, retrain them, or prosecute them.

When an algorithm produces an error, there is no one to answer for it. That is the real threat of artificial intelligence: not that machines will think for themselves, but that we will treat algorithmic outputs as decisions rather than as intelligence that informs human decisions. The danger is not the technology itself, which can surface patterns humans miss and process data at scales humans cannot match. The danger is forgetting that someone human must be responsible when things go wrong.

🎬 Clip from “Boston Blue” (Season 1, Episode 1: Premiere Episode)
Created by Aaron Allen (showrunner)
Starring Donnie Wahlberg, Maggie Lawson, Sonequa Martin-Green, Marcus Scribner

Produced by CBS Studios / Paramount Global
📺 Original air date: October 17 2025 on CBS
All rights © CBS / Paramount Global — used under fair use for commentary and criticism.

Who Decides? That Question Defines Everything.

The current conversation about AI governance misses the essential point. People debate whether AI should be “in the loop” or whether humans should review AI recommendations. Those questions assume AI makes decisions and humans check them.

That assumption is backwards.

In properly governed systems, humans make decisions. AI provides intelligence that helps humans decide better. The distinction is not semantic. It determines who holds authority and who bears accountability. As the National Institute of Standards and Technology’s AI Risk Management Framework (2023) emphasizes, trustworthy AI requires “appropriate methods and metrics to evaluate AI system trustworthiness” alongside documented accountability structures where specific humans remain answerable for outcomes.

Consider the difference in the Robert Williams case. In 2020, Detroit police arrested Williams after a facial recognition system matched his driver’s license photo to security footage of a shoplifting suspect. Williams was held for 30 hours. His wife watched police take him away in front of their daughters. He was innocent (Hill, 2020).

Here is what happened. An algorithm produced a match. A detective trusted that match. An arrest followed. When Williams sued, responsibility scattered. The algorithm vendor said they provided a tool, not a decision. The police said they followed the technology. The detective said they relied on the system. Everyone pointed elsewhere.

Now consider how it should have worked under the framework proposed in the Algorithmic Accountability Act of 2025, which requires documented impact assessments for any “augmented critical decision process” where automated systems influence significant human consequences (U.S. Congress, 2025).

An algorithm presents multiple potential matches with confidence scores. It shows which faces are similar and by what measurements. The algorithm flags that confidence is lower for this particular demographic. The detective reviews those options alongside other evidence. The detective notes in a documented record that match confidence is marginal. The detective documents that without corroborating evidence, match quality alone does not establish probable cause. The detective decides whether action is justified.

If that decision is wrong, accountability is clear. The detective made the call. The algorithm provided analysis. The human decided. The documentation shows what the detective considered and why they chose as they did. The record is auditable, traceable, and tied to a specific decision-maker.

That is the structure we need. Not AI making decisions that humans approve, but humans making decisions with AI providing intelligence. The technology augments human judgment. It does not replace it.

Accountability Requires Documented Decision-Making

When things go wrong with AI systems, investigations fail because no one can trace who decided what, or why. Organizations claim they had oversight, but cannot produce evidence showing which specific person evaluated the decision, what criteria they applied, what alternatives they considered, or what reasoning justified their choice.

That evidential gap is not accidental. It is structural. When AI produces outputs and humans simply approve or reject them, the approval becomes passive. The human becomes a quality control inspector on an assembly line rather than a decision-maker. The documentation captures whether someone said yes or no, but not what judgment process led to that choice.

Effective governance works differently. It structures decisions around checkpoints where humans must actively claim decision authority. Checkpoint governance is a framework where identifiable humans must document and own decisions at defined stages of AI use. This approach operationalizes what international frameworks mandate: UNESCO’s Recommendation on the Ethics of Artificial Intelligence (2024) requires “traceability and explainability” with maintained human accountability for any outcomes affecting rights, explicitly stating that systems lacking human oversight lack ethical legitimacy.

At each checkpoint, the system requires the human to document not just what they decided, but how they decided. What options did the AI present. What alternatives were considered. Was there dissent about the approach. What criteria were applied. What reasoning justified this choice over others.

That documentation transforms oversight from theatrical to substantive. It creates what decision intelligence frameworks call “audit trails tied to business KPIs,” pairing algorithmic outputs with human checkpoint approvals and clear documentation of who, what, when, and why for every consequential outcome (Approveit, 2025).

What Checkpoint Governance Looks Like

The framework is straightforward. Before AI-informed decisions can proceed, they must pass through structured checkpoints where specific humans hold decision authority. This model directly implements the “Govern, Map, Measure, Manage” cycle that governance standards prescribe (NIST, 2023). At each checkpoint, four things happen:

AI contributes intelligence. The system analyzes data, identifies patterns, generates options, and presents findings. This is what AI does well: processing more information faster than humans can and surfacing insights humans might miss. Research shows that properly deployed AI can reduce certain forms of human bias by standardizing evaluation criteria and flagging inconsistencies that subjective judgment overlooks (McKinsey & Company, 2025).

The output is evaluated against defined criteria. These criteria are explicit and consistent. What makes a facial recognition match credible. What evidence standard justifies an arrest. What level of confidence warrants action. The criteria prevent ad hoc judgment and support consistent decision-making across different reviewers.

A designated human arbitrates. This person reviews the evaluation, applies judgment informed by context the AI cannot access, and decides. Not approves or rejects—decides. The human is the decision-maker. The AI provided intelligence. The human decides what it means and what action follows. High-performing organizations embed these “accountability pathways tied to every automated decision, linking outputs to named human approvers” (McKinsey & Company, 2025).

The decision is documented. The record captures what was evaluated, what criteria applied, what the human decided, and most importantly, why. What alternatives did they consider. Was there conflicting evidence. Did they override a score because context justified it. What reasoning supports this decision.

That four-stage process keeps humans in charge while making their decision-making auditable. It acknowledges a complexity: in sophisticated AI systems producing multi-factor risk assessments or composite recommendations, the line between “intelligence” and “decision” can blur. A credit scoring algorithm that outputs a single approval recommendation functions differently than one that presents multiple risk factors for human synthesis. Checkpoint governance addresses this by requiring that wherever the output influences consequential action, a human must claim ownership of that action through documented reasoning.

The Difference Accountability Makes

Testing by the National Institute of Standards and Technology (2019) found that some facial recognition systems were up to 100 times less accurate for darker-skinned faces than lighter ones. The Williams case was not an anomaly. It was a predictable outcome of that accuracy gap. Subsequent NIST testing in 2023 confirmed ongoing accuracy disparities across demographic groups.

But the deeper failure was not technical. It was governance. Without structured checkpoints, no one had to document what alternatives they considered before acting on the match. No one had to explain why the match quality justified arrest given the known accuracy disparities. No one had to record whether anyone raised concerns.

If checkpoint governance had been in place, meeting the standards now proposed in the Algorithmic Accountability Act of 2025, the decision process would have looked different.

The algorithm presents multiple potential matches. It flags that confidence is lower for this particular face. A detective reviews the matches alongside other evidence. The detective notes in the record that match confidence is marginal. The detective documents that without corroborating evidence, match quality alone does not establish probable cause. The detective decides that further investigation is needed before arrest. This decision is logged with the detective’s identifier, timestamp, and rationale.

If the detective instead decides the match justifies arrest despite the lower confidence, they must document why. What other evidence exists. What makes this case an exception. That documentation creates accountability. If the arrest proves wrong, investigators can review the detective’s reasoning and determine whether the decision process was sound.

That is what distinguishes human error from systemic failure. Humans make mistakes, but when decisions are documented, those mistakes can be reviewed, learned from, and corrected. When decisions are not documented, the same mistakes repeat because no one can trace why they occurred.

Why Algorithms Cannot Be Held Accountable

A sentencing algorithm used across the United States, called COMPAS, was found to label Black defendants as high risk at twice the rate of white defendants who did not reoffend (Angwin et al., 2016). When researchers exposed this bias, the system continued operating. No one faced consequences. No one was sanctioned.

Recognizing these failures, some jurisdictions have begun implementing alternatives. The Algorithmic Accountability Act of 2025, introduced by Representative Yvette Clarke, explicitly targets automated systems in “housing, employment, credit, education” and requires deployers to conduct and record algorithmic impact assessments documenting bias, accuracy, explainability, and downstream effects (Clarke, 2025). The legislation provides Federal Trade Commission enforcement mechanisms for incomplete or falsified assessments, creating the accountability structure that earlier deployments lacked.

That regulatory evolution reflects the fundamental difference between human and algorithmic decision-making. Humans can be held accountable for their errors, which creates institutional pressure to improve. Algorithms operate without that pressure because no identifiable person bears responsibility for their outputs. Even when algorithms are designed to reduce human bias through standardized criteria and consistent application, they require human governance to ensure those criteria themselves remain fair and contextually appropriate.

Courts already understand this principle in other contexts. When a corporation harms someone, the law does not excuse executives by saying they did not personally make every operational choice. The law asks whether they established reasonable systems to prevent harm. If they did not, they are liable.

AI governance must work the same way. Someone must be identifiable and answerable for decisions AI informs. That person must be able to show they followed reasonable process. They must be able to demonstrate what alternatives they considered, what criteria they applied, and why their decision was justified.

Checkpoint governance creates that structure. It ensures that for every consequential decision, there is a specific human whose judgment is documented and whose reasoning can be examined.

Building the System of Checks and Balances

Modern democracies are built on checks and balances. No single person has unchecked authority. Power is distributed. Decisions are reviewed. Mistakes have consequences. That structure does not eliminate error, but it prevents error from proceeding uncorrected.

AI governance must follow the same principle. Algorithmic outputs should not proceed unchecked to action. Their insights must inform human decisions made at structured checkpoints where specific people hold authority and bear responsibility. Five governance frameworks now converge on this approach, establishing consensus pillars of transparency, data privacy, bias management, human oversight, and audit mechanisms (Informs Institute, 2025).

There are five types of checkpoints that high-stakes AI deployments need:

Intent Checkpoints examine why a system is being created and who it is meant to serve. A facial recognition system intended to find missing children is different from one intended to monitor peaceful protesters. Intent shapes everything that follows. At this checkpoint, a specific person takes responsibility for ensuring the system serves its stated purpose without causing unjustified harm. The European Union’s AI Act (2024) codifies this requirement through mandatory purpose specification and use-case limitation for high-risk applications.

Data Checkpoints require documentation of where training data came from and who is missing from it. The Williams case happened because facial recognition was trained primarily on lighter-skinned faces. The data gap created the accuracy gap. At this checkpoint, a specific person certifies that data has been reviewed for representation gaps and historical bias. Organizations implementing this checkpoint have identified and corrected dataset imbalances before deployment, preventing downstream discrimination.

Model Checkpoints verify testing for fairness and reliability across different populations. Testing is not one-time but continuous, because system performance changes as the world changes. At this checkpoint, a specific person certifies that the model performs within acceptable error ranges for all affected groups. Ongoing monitoring at this checkpoint has detected concept drift and performance degradation in operational systems, triggering recalibration before significant harm occurred.

Use Checkpoints define who has authority to act on system outputs and under what circumstances. A facial recognition match should not lead directly to arrest but to investigation. The human detective remains responsible for deciding whether evidence justifies action. At this checkpoint, a specific person establishes use guidelines and trains operators on the system’s limitations. Directors and board members increasingly recognize this as a governance imperative, with 81% of companies acknowledging governance lag despite widespread AI deployment (Directors & Boards, 2025).

Impact Checkpoints measure real-world outcomes and correct problems as they emerge. This is where accountability becomes continuous, not just a pre-launch formality. At this checkpoint, a specific person reviews outcome data, identifies disparities, and has authority to modify or suspend the system if harm is occurring. This checkpoint operationalizes what UNESCO (2024) describes as the obligation to maintain human accountability throughout an AI system’s operational lifecycle.

Each checkpoint has the same essential requirement: a designated human makes a decision and documents what alternatives were considered, whether there was dissent, what criteria were applied, and what reasoning justified the choice. That documentation creates the audit trail that makes accountability enforceable.

The Implementation Reality: Costs and Complexities

Checkpoint governance is not without implementation challenges. Organizations adopting this framework should anticipate three categories of burden.

Structural costs include defining decision rights, specifying evaluation criteria with concrete examples, building logging infrastructure, and training personnel on checkpoint protocols. These are one-time investments that require thoughtful design.

Operational costs include the time required for human arbitration at each checkpoint, periodic calibration to prevent criteria from becoming outdated, and maintaining audit trail systems. These are recurring expenses that scale with deployment scope.

Cultural costs involve shifting organizational mindsets from “AI approves, humans review” to “humans decide, AI informs.” This requires executive commitment and sustained attention to prevent automation bias, where reviewers gradually default to approving AI recommendations without critical evaluation.

These costs are real. They represent intentional friction introduced into decision processes. The question is whether that friction is justified. For high-stakes decisions in regulated industries, for brand-critical communications, for any context where single failures create significant harm to individuals or institutional reputation, the accountability benefits justify the implementation burden. For lower-stakes applications where rapid iteration matters more than individual decision traceability, lighter governance or even autonomous operation may be appropriate.

The framework is risk-proportional by design. Organizations can implement comprehensive checkpoints where consequences are severe and streamlined governance where they are not. The principle remains constant: someone specific must be responsible, their decision process must be documented, and they must be answerable when things go wrong.

What Detective Reagan Teaches About Accountability

Reagan’s instinct to question the facial recognition match is more than good detective work. It is the pause that creates accountability. That moment of hesitation is the checkpoint where a human takes responsibility for what happens next.

His insight holds the key. The tech is just a tool. Tools do not bear responsibility. People do. The question is whether we will build systems that make responsibility clear, or whether we will let AI diffuse responsibility until no one can be held to account for decisions.

We already know what happens when power operates without accountability. The Williams case shows us. The COMPAS algorithm shows us. Every wrongful arrest, every biased loan denial, every discriminatory hiring decision made by an insufficiently governed AI system shows us the same thing: without structured accountability, even good intentions produce harm.

What This Means in Practice

Checkpoint governance is not theoretical. Organizations are implementing it now. The European Union AI Act (2024) requires impact assessments and human oversight for high-risk systems. The Algorithmic Accountability Act of 2025 establishes enforcement mechanisms for U.S. federal oversight. Some states mandate algorithmic audits. Some corporations have established AI review boards with authority to stop deployments.

But voluntary adoption alone is insufficient. Accountability requires structure. It requires designated humans with decision authority at specific checkpoints. It requires documentation that captures the decision process, not just the decision outcome. It requires consequences when decision-makers fail to meet their responsibility.

The structure does not need to be identical across all contexts. High-stakes decisions in regulated industries (finance, healthcare, criminal justice) require comprehensive checkpoints at every stage. Lower-stakes applications can use lighter governance. The principle remains constant: someone specific must be responsible, their decision process must be documented, and they must be answerable when things go wrong.

That is not asking AI to be perfect. It is asking the people who deploy AI to be accountable.

Humans make mistakes. Judges err. Engineers miscalculate. Doctors misdiagnose. But those professions have accountability mechanisms that create institutional pressure to learn and improve. When a judge makes a sentencing error, the decision can be appealed and the judge’s reasoning reviewed. When an engineer’s design fails, investigators examine whether proper procedures were followed. When a doctor’s diagnosis proves wrong, medical boards review whether the standard of care was met.

AI needs the same accountability structure. Not because AI should be held to a higher standard than humans, but because AI should be held to the same standard. Decisions that affect people’s lives should be made by humans who can be held responsible for their choices.

The Path Forward

If we build checkpoint governance into AI deployment, we have nothing to fear from the technology. The algorithms will do what they have always done: process information faster and more comprehensively than humans can, surface patterns that human attention might miss, and apply consistent criteria that reduce certain forms of subjective bias. But decisions will remain human. Accountability will remain clear. When mistakes happen, we will know who decided, what they considered, and why they chose as they did.

If we do not build that structure, the risk is not the algorithm. The risk is the diffusion of accountability that lets everyone point elsewhere when things go wrong. The risk is the moment when harm occurs and no one can be identified as responsible.

Detective Reagan is right. The tech is just a tool, but only when someone accepts responsibility for how it is used. Someone must wield it. Someone must decide what it means and what action follows. Someone must answer when the decision proves wrong.

Checkpoint governance ensures that someone exists. It makes them identifiable. It documents their reasoning. It creates the accountability that lets us trust AI-informed decisions because we know humans remain in charge.

That is the system of checks and balances artificial intelligence needs. Not to slow progress, but to direct it. Not to prevent innovation, but to ensure innovation serves people without leaving them defenseless when things go wrong.

The infrastructure is emerging. The Algorithmic Accountability Act establishes federal oversight. The EU AI Act provides a regulatory template. UNESCO’s ethical framework sets international norms. Corporate governance is evolving to match technical capability with human accountability.

The question now is execution. Will organizations implement checkpoint governance before the next Williams case, or after. Will they build audit trails before regulators demand them, or in response to enforcement. Will they treat accountability as a design principle, or as damage control.

Detective Reagan’s pause should be systemic, not individual. It should be built into every consequential AI deployment as structure, not left to the judgment of individual operators who may or may not question what the algorithm presents.

The tech is just a tool. We are responsible for ensuring it remains one.

References

Algorithmic Accountability Act of 2025, S.2164, 119th Congress (2025). https://www.congress.gov/bill/119th-congress/senate-bill/2164/text
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Approveit. (2025, October 16). AI Decision-Making Facts (2025): Regulation, Risk & ROI. https://approveit.today/blog/ai-decision-making-facts-(2025)-regulation-risk-roi
Clarke, Y. (2025, September 19). Clarke introduces bill to regulate AI’s control over critical decision-making in housing, employment, education, and more [Press release]. https://clarke.house.gov/clarke-introduces-bill-to-regulate-ais-control-over-critical-decision-making-in-housing-employment-education-and-more/
Directors & Boards. (2025, June 26). Decision-making in the age of AI. https://www.directorsandboards.com/board-issues/ai/decision-making-in-the-age-of-ai/
European Commission. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Hill, K. (2020, June 24). Wrongfully Accused by an Algorithm. The New York Times. https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html
Informs Institute. (2025, July 21). Navigating AI regulations: What businesses need to know in 2025. https://pubsonline.informs.org/do/10.1287/LYTX.2025.03.10/full/
McKinsey & Company. (2025, June 3). When can AI make good decisions? The rise of AI corporate citizens. https://www.mckinsey.com/capabilities/operations/our-insights/when-can-ai-make-good-decisions-the-rise-of-ai-corporate-citizens
National Institute of Standards and Technology. (2019). Face Recognition Vendor Test (FRVT). https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
UNESCO. (2024, September 25). Recommendation on the Ethics of Artificial Intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics

Measuring Collaborative Intelligence: How Basel and Microsoft’s 2025 Research Advances the Science of Human Cognitive Amplification

October 12, 2025 by Basil Puglisi Leave a Comment

Basel and Microsoft proved AI boosts productivity and learning. The Human Enhancement Quotient explains what those metrics miss: the measurement of human intelligence itself.

Opening Framework

Two major studies published in October 2025 prove AI collaboration boosts productivity and learning. What they also reveal: we lack frameworks to measure whether humans become more intelligent through that collaboration. This is the measurement gap the Human Enhancement Quotient addresses.

We are measuring the wrong things. Academic researchers track papers published and journal rankings. Educational institutions measure test scores and completion rates. Organizations count tasks completed and time saved.

None of these metrics answer the question that matters most: Are humans becoming more capable through AI collaboration, or just more productive?

This is not a semantic distinction. Productivity measures output. Intelligence measures transformation. A researcher who publishes 36% more papers may be writing faster without thinking deeper. A student who completes assignments more quickly may be outsourcing cognition rather than developing it.

The difference between acceleration and advancement is the difference between borrowing capability and building it. Until we can measure that difference, we cannot govern it, improve it, or understand whether AI collaboration enhances human intelligence or merely automates human tasks.

The Evidence Arrives: Basel and Microsoft

Basel’s Contribution: Productivity Without Cognitive Tracking

University of Basel (October 2, 2025)
Can GenAI Improve Academic Performance? (IZA Discussion Paper No. 17526)

Filimonovic, Rutzer, and Wunsch delivered rigorous quantitative evidence using author-level panel data across thousands of researchers. Their difference-in-differences approach with propensity score matching provides methodological rigor the field needs. The findings are substantial: GenAI adoption correlates with productivity increases of 15% in 2023, rising to 36% by 2024, with modest quality improvements measured through journal impact factors.

The equity findings are particularly valuable. Early-career researchers, those in technically complex subfields, and authors from non-English-speaking countries showed the strongest benefits. This suggests AI tools may lower structural barriers in academic publishing.

What the study proves: Productivity gains are real, measurable, and significant.

What the study cannot measure: Whether those researchers are developing stronger analytical capabilities, whether their reasoning quality is improving, or whether the productivity gains reflect permanent skill enhancement versus temporary scaffolding.

As the authors note in their conclusion: “longer-term equilibrium effects on research quality and innovation remain unexplored.”

This is not a limitation of Basel’s research. It is evidence of the measurement category that does not yet exist.

Microsoft’s Contribution: Learning Outcomes Without Cognitive Development Metrics

Microsoft Research (October 7, 2025)
Learning Outcomes with GenAI in the Classroom (Microsoft Technical Report MSR-TR-2025-42)

Walker and Vorvoreanu’s comprehensive review across dozens of educational studies provides essential guidance for educators. Their synthesis documents measurable improvements in writing efficiency and learning engagement while identifying critical risks: overconfidence in shallow skill mastery, reduced retention, and declining critical thinking when AI replaces rather than supplements human-guided reflection.

The report’s four evidence-based guidelines are immediately actionable: ensure student readiness, teach explicit AI literacy, use AI as supplement not replacement, and design interventions fostering genuine engagement.

What the study proves: Learning outcomes depend critically on structure and oversight. Without pedagogical guardrails, productivity often comes at the expense of comprehension.

What the study cannot measure: Which specific cognitive processes are enhanced or degraded under different collaboration structures. Whether students are developing transferable analytical capabilities or becoming dependent on AI scaffolding. How to quantify the cognitive transformation itself.

As the report acknowledges: “isolating AI’s specific contribution to cognitive development” remains methodologically complex.

Again, this is not a research flaw. It is proof that our measurement tools lag behind our deployment reality.

Why Intelligence Measurement Matters Now

Together, these studies establish that AI collaboration produces measurable effects on human performance. What they also reveal is how much we still cannot see.

Basel tracks velocity and destination: papers published, journals reached. Microsoft tracks outcomes: scores earned, assignments completed. Neither can track the cognitive journey itself. Neither can answer whether the collaboration is building human capability or borrowing machine capability.

Organizations are deploying AI collaboration tools across research, education, and professional work without frameworks to measure cognitive transformation. Universities integrate AI into curricula without metrics for reasoning development. Employers hire for “AI-augmented roles” without assessing collaborative intelligence capacity.

The gap is not just academic. It is operational, ethical, and urgent.

“We measure what machines help us produce. We still need to measure what humans become through that collaboration.”
— Basil Puglisi, MPA

Enter Collaborative Intelligence Measurement

The Human Enhancement Quotient quantifies what Basel and Microsoft cannot: cognitive transformation in human-AI collaboration environments.

HEQ does not replace productivity metrics or learning assessments. It measures a different dimension entirely: how human intelligence changes through sustained AI partnership.

Let me demonstrate with a concrete scenario.

A graduate student uses ChatGPT to write a literature review.

Basel measures: Papers published, citation patterns, journal placement.

Microsoft measures: Assignment completion time, grade received, engagement indicators.

HEQ measures four cognitive dimensions:

Cognitive Amplification Score (CAS)

After three months of AI-assisted research, does the student integrate complex theoretical frameworks faster? Can they identify connections between disparate sources more efficiently? This measures cognitive acceleration, not output speed. Does the processing itself improve?

Evidence-Analytical Index (EAI)

Does the student critically evaluate AI-generated citations before using them? Do they verify claims independently? Do they maintain transparent documentation distinguishing AI contributions from independent analysis? This tracks reasoning quality and intellectual integrity in augmented environments.

Collaborative Intelligence Quotient (CIQ)

When working with peers on joint projects, does the student effectively synthesize AI outputs with human discussion? Can they explain AI contributions to committee members in ways that strengthen arguments rather than obscure thinking? This measures integration capability across human and machine perspectives.

Adaptive Growth Rate (AGR)

Six months later, working on a new topic without AI assistance, is the student demonstrably more capable at literature synthesis than before using AI? Did the collaboration build permanent analytical skill or provide temporary scaffolding? This tracks whether enhancement persists when the tool is removed.

Productivity measures what we produce. Intelligence measures what we become. The difference is everything.

These dimensions complement Basel and Microsoft’s findings while measuring what they cannot. If a researcher publishes 36% more papers (Basel’s metric) but shows declining source evaluation rigor (HEQ’s EAI), we understand the true cost of that productivity. If a student completes assignments faster (Microsoft’s metric) but demonstrates reduced independent capability afterward (HEQ’s AGR), we see the difference between acceleration and advancement.

Applying this framework retrospectively to Basel’s equity findings, we could test whether non-English-speaking researchers’ productivity gains correlate with improved analytical capability or simply faster translation assistance, distinguishing genuine cognitive enhancement from tool-mediated efficiency.

What Makes Collaborative Intelligence Measurable

The question is not whether AI helps humans produce more. Basel and Microsoft prove it does. The question is whether AI collaboration makes humans more intelligent in measurable, persistent ways.

HEQ treats collaboration as a cognitive environment that can be quantified across four dimensions. These metrics are tested across multiple AI platforms (ChatGPT, Claude, Gemini) with protocols that adapt to privacy constraints and memory limitations.

Privacy and platform diversity remain methodological challenges. HEQ acknowledges this transparently. Long-chat protocols measure deep collaboration where conversation history permits. Compact protocols run standardized assessments where privacy isolation requires it. The framework prioritizes measurement validity over platform convenience.

This is not theoretical modeling. It is operational measurement for real-world deployment.

The Three-Layer Intelligence Framework

What comes next is integration. Basel, Microsoft, and HEQ measure different aspects of the same phenomenon: human capability in AI-augmented environments.

These layers together form a complete intelligence measurement system:

Outcome Intelligence

Papers published, citations earned, journal rankings (Basel approach)
Test scores, completion rates, engagement metrics (Microsoft approach)
Validates that collaboration produces measurable effects

Process Intelligence

Cognitive amplification, reasoning quality, collaborative capacity (HEQ approach)
Tracks how humans change through the collaboration itself
Distinguishes enhancement from automation

Governance Intelligence

Equity measures, skill transfer, accessibility (integrated approach)
Ensures enhancement benefits are distributed fairly
Validates training effectiveness and identifies intervention needs

This three-layer framework lets us answer questions none of the current approaches addresses alone:

Do productivity gains come with cognitive development or at its expense? Which collaboration structures build permanent capability versus temporary scaffolding? How do we train for genuine enhancement rather than skilled tool use? When does AI collaboration amplify human intelligence and when does it simply automate human tasks?

Why “Generative AI” Obscures This Work

A brief note on terminology, because language shapes measurement.

When corporations and media call these systems “Generative AI,” they describe a commercial product, not a cognitive reality. Large language models perform statistical sequence prediction. They reflect and recombine human meaning at scale, weighted by probability, optimized for coherence.

Emily Bender and colleagues warned in On the Dangers of Stochastic Parrots that these systems produce fluent text without grounded understanding. The risk is not that machines begin to think, but that humans forget they do not.

If precision matters, the better term is Reflective AI: systems that mirror human input at scale. “Generative” implies autonomy. Autonomy sells investment. But it obscures the measurement question that actually matters.

The question is not what machines can generate. The question is what humans become when working with machines that reflect human meaning back at scale. That is an intelligence question. That is what HEQ measures.

Collaborative Intelligence Governance

Both Basel and Microsoft emphasize governance as essential. Basel’s authors call for equitable access policies supporting linguistically marginalized researchers. Microsoft’s review stresses pedagogical guardrails and explicit AI literacy instruction.

These governance recommendations rest on measurement. You cannot govern what you cannot measure. You cannot improve what you do not track.

Traditional governance asks: Are we using AI responsibly?

Intelligence governance asks: Are humans becoming more capable through AI use?

That second question requires measurement frameworks that track cognitive transformation. Without them, governance becomes guesswork. Organizations implement AI literacy training without metrics for reasoning development. Institutions adopt collaboration tools without frameworks for measuring genuine enhancement versus skilled automation.

HEQ moves from research contribution to governance necessity when we recognize that collaborative intelligence is the governance challenge.

The framework provides:

Capability Assessment: Quantify individual readiness for AI-augmented roles rather than assuming uniform benefit from training.

Training Validation: Measure whether AI collaboration programs build permanent capability or temporary productivity through pre/post cognitive assessment.

Equity Monitoring: Track whether enhancement benefits distribute fairly or concentrate among already-advantaged populations.

Intervention Design: Identify which cognitive processes require protection or development under specific collaboration structures.

This is not oversight of AI tools. This is governance of intelligence itself in collaborative environments.

Immediate Implementation Steps

For universities: Pilot HEQ assessment alongside existing outcome metrics in one department for one semester. Compare productivity gains with cognitive development measures.

For employers: Include collaborative intelligence capacity in job descriptions requiring AI tool use. Assess candidates on reasoning quality and adaptive growth, not just tool proficiency.

For training providers: Measure pre/post HEQ scores to demonstrate actual capability enhancement versus productivity gains. Use cognitive metrics to validate training effectiveness and justify continued investment.

What the Research Community Needs Next

As someone who builds measurement frameworks rather than commentary, I see these studies as allies in defining essential work.

For the Basel team: Your equity findings suggest early-career and non-English-speaking researchers benefit most from AI tools. The natural follow-up is whether that benefit reflects permanent capability enhancement or temporary productivity scaffolding. Longitudinal cognitive measurement using frameworks like HEQ could distinguish these and validate your impressive productivity findings with transformation data.

For the Microsoft researchers: Your emphasis on structure and oversight is exactly right. The follow-up question is which specific cognitive processes are protected or degraded under different scaffolding approaches. Process measurement frameworks could guide your intervention design recommendations with quantitative cognitive data.

For the broader research community: We now have evidence that AI collaboration affects human performance. The question becomes whether we can measure those effects at the level that matters: cognitive transformation itself.

This is not about replacing outcome metrics. It is about adding the intelligence layer that explains why those outcomes move as they do.

Closing Framework

The future of intelligence will not be machine or human. It will be measured by how well we understand what happens when they collaborate, and whether that collaboration builds capability or merely borrows it.

Basel and Microsoft mapped the outcomes. They proved collaboration produces measurable effects on productivity and learning. They also proved we lack frameworks to measure the cognitive transformation beneath those effects.

That is the measurement frontier. That is where HEQ operates. And that is what collaborative intelligence governance requires.

We can count papers published and test scores earned. Now we need to measure whether humans become more intelligent through the collaboration itself, with the same precision we expect from every other science.

The work ahead is not about building smarter machines. It is about learning to measure how intelligence evolves when humans and systems learn together.

Not productivity. Not outcomes. Intelligence itself.

References

Filimonovic, D., Rutzer, C., & Wunsch, C. (2025, October 2). Can GenAI Improve Academic Performance? Evidence from the Social and Behavioral Sciences. University of Basel / IZA Discussion Paper No. 17526. arXiv:2510.02408
Walker, K., & Vorvoreanu, M. (2025, October 7). Learning outcomes with GenAI in the classroom: A review of empirical evidence. Microsoft Technical Report MSR-TR-2025-42. Read the full report
Puglisi, B. (2025, September 28). The Human Enhancement Quotient: Measuring Cognitive Amplification Through AI Collaboration. https://basilpuglisi.com/the-human-enhancement-quotient-heq-measuring-cognitive-amplification-through-ai-collaboration-draft/
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ACM FAccT 2021

From Measurement to Mastery: How FID Evolved into the Human Enhancement Quotient

October 6, 2025 by Basil Puglisi Leave a Comment

When I built the Factics Intelligence Dashboard, I thought it would be a measurement tool. I designed it to capture how human reasoning performs when partnered with artificial systems. But as I tested FID across different platforms and contexts, the data kept showing me something unexpected. The measurement itself was producing growth. People were not only performing better when they used AI, they were becoming better thinkers.

The Factics Intelligence Dashboard, or FID, was created to measure applied intelligence. It mapped how humans think, learn, and adapt when working alongside intelligent systems rather than in isolation. Its six domains (Verbal, Analytical, Creative, Strategic, Emotional, and Adaptive) were designed to evaluate performance as evidence of intelligence. It showed how collaboration could amplify precision, clarity, and insight (Puglisi, 2025a).

As the model matured, it became clear that measurement was not enough. Intelligence was not a static attribute that could be captured in a snapshot. It was becoming a relationship. Every collaboration with AI enhanced capability. Every iteration made the user stronger. That discovery shifted the work from measuring performance to measuring enhancement. The result became the Human Enhancement Quotient, or HEQ (Puglisi, 2025b).

FID asked, How do you think? HEQ asks, How far can you grow?

While FID provided a structured way to observe intelligence in action, HEQ measures how that intelligence evolves through continuous interaction with artificial systems. It transforms the concept of measurement into one of growth. The goal is not to assign a score but to map the trajectory of enhancement.

This reflects the transition from IQ as a fixed measure of capability to intelligence as a living process of amplification. The foundation for this shift can be traced to the same thinkers who redefined cognition long before AI entered the equation. Gardner proved intelligence is multiple (1983). Sternberg reframed it as analytical, creative, and practical (1985). Goleman showed it could be emotional. Dweck demonstrated it could grow. Kasparov revealed it could collaborate. Each idea pointed to the same truth: intelligence is not what we possess. It is what we develop.

HEQ condensed FID’s six measurable domains into four dimensions that reflect dynamic enhancement over time rather than static skill at a moment.

How HEQ Builds on FID

Mapping FID domains to HEQ dimensions and their purpose.
FID (2025)	HEQ (2025 to 2026)	Purpose
Verbal / Linguistic	Cognitive Adaptive Speed (CAS)	How quickly humans process, connect, and express ideas when supported by AI
Analytical / Logical	Ethical Alignment Index (EAI)	How reasoning aligns with transparency, accountability, and fairness
Creative + Strategic	Collaborative Intelligence Quotient (CIQ)	How effectively humans co-create and integrate insight with AI partners
Emotional + Adaptive	Adaptive Growth Rate (AGR)	How fast and sustainably human capability increases through ongoing collaboration

Where FID produced a snapshot of capability, HEQ produces a trajectory of progress. It introduces a quantitative measure of how human performance improves through repeated AI interaction.

Preliminary testing across five independent AI systems suggested a reliability coefficient near 0.96 [PROVISIONAL: Internal dataset, peer review pending]. This consistency confirmed that the model could track cognitive amplification across architectures. HEQ takes that finding further by measuring how the collaboration itself transforms the human contributor.

HEQ is designed to assess four key aspects of human and AI synergy.

Cognitive Adaptive Speed (CAS) tracks how rapidly users integrate new concepts when guided by AI reasoning.

Ethical Alignment Index (EAI) measures how decision-making maintains transparency and integrity within machine augmented systems.

Collaborative Intelligence Quotient (CIQ) evaluates how effectively humans coordinate across perspectives and technologies to produce creative solutions.

Adaptive Growth Rate (AGR) calculates how much individual capability expands through continued human and AI collaboration.

Together, these dimensions form a single composite score representing a user’s overall enhancement potential. While IQ measures cognitive possession, HEQ measures cognitive acceleration.

The journey from FID to HEQ reflects the evolution of modern intelligence itself. FID proved that collaboration changes how we perform. HEQ proves that collaboration changes who we become.

FID captured the interaction. HEQ captures the transformation.

This shift matters because intelligence in the AI era is not a fixed property. It is a living partnership. The moment we begin working with intelligent systems, our own intelligence expands. HEQ provides a way to measure that growth, validate it, and apply it as a framework for strategic learning and ethical governance.

This research completes a circle that began with Factics in 2012. FID quantified performance. HEQ quantifies progress. Together they form the measurement core of the Growth OS ecosystem, connecting applied intelligence, ethical reasoning, and adaptive learning into a single integrated model for advancement in the age of artificial intelligence.

References

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
Carter, N. [@nic__carter]. (2025, April 15). I’ve noticed a weird aversion to using AI … it seems like a massive self-own to deduct yourself 30 points of IQ because you don’t like the tech [Post]. X. https://twitter.com/nic__carter/status/1780330420201979904
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
Gawdat, M. [@mgawdat]. (2025, August 4). Using AI is like borrowing 50 IQ points [Post]. X. [PROVISIONAL: Quote verified through secondary coverage at https://www.tekedia.com/former-google-executive-mo-gawdat-warns-ai-will-replace-everyone-even-ceos-and-podcasters/. Direct tweet archive not located.]
Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. Bantam Books.
Kasparov, G. (2017). Deep thinking: Where machine intelligence ends and human creativity begins. PublicAffairs.
Kasparov, G. (2021, March). How to build trust in artificial intelligence. Harvard Business Review https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it
Puglisi, B. C. (2025a). From metrics to meaning: Building the Factics Intelligence Dashboard https://basilpuglisi.com/from-metrics-to-meaning-building-the-factics-intelligence-dashboard
Puglisi, B. C. (2025b). The Human Enhancement Quotient: Measuring cognitive amplification through AI collaboration https://basilpuglisi.com/the-human-enhancement-quotient-heq-measuring-cognitive-amplification-through-ai-collaboration-draft
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.

Why I Am Facilitating the Human Enhancement Quotient

October 2, 2025 by Basil Puglisi Leave a Comment

Human Enhancement Quotient, HEQ, AI collaboration, AI measurement, AI ethics, AI training, AI education, digital intelligence, Basil Puglisi, human AI partnership

The idea that AI could make us smarter has been around for decades. Garry Kasparov was one of the first to popularize it after his legendary match against Deep Blue in 1997. Out of that loss he began advocating for what he called “centaur chess,” where a human and a computer play as a team. Kasparov argued that a weak human with the right machine and process could outperform both the strongest grandmasters and the strongest computers. His insight was simple but profound. Human intelligence is not fixed. It can be amplified when paired with the right tools.

Fast forward to 2025 and you hear the same theme in different voices. Nic Carter claimed rejecting AI is like deducting 30 IQ points from yourself. Mo Gawdat framed AI collaboration as borrowing 50 IQ points, or even thousands, from an artificial partner. Jack Sarfatti went further, saying his effective IQ had reached 1,000 with Super Grok. These claims may sound exaggerated, but they show a common belief taking hold. People feel that working with AI is not just a productivity boost, it is a fundamental change in how smart we can become.

Curious about this, I asked ChatGPT to reflect on my own intelligence based on our conversations. The model placed me in the 130 to 145 range, which was striking not for the number but for the fact that it could form an assessment at all. That moment crystallized something for me. If AI can evaluate how it perceives my thinking, then perhaps there is a way to measure how much AI actually enhances human cognition.

Then the conversation shifted from theory to urgency. Microsoft announced layoffs between 6,000 and 15,000 employees tied directly to its AI investment strategy. Executives framed the cuts around embracing AI, with the implication that those who could not or would not adapt were left behind. Accenture followed with even clearer language. Julie Sweet said outright that staff who cannot be reskilled on AI would be “exited.” More than 11,000 had already been laid off by September, even as the company reskilled over half a million in generative AI fundamentals.

This raised the central question for me. How do they know who is or is not AI trainable. On what basis can an organization claim that someone cannot be reskilled. Traditional measures like IQ, SAT, or GRE tell us about isolated ability, but they do not measure whether a person can adapt, learn, and perform better when working with AI. Yet entire careers and livelihoods are being decided on that assumption.

At the same time, I was shifting my own work. My digital marketing blogs on SEO, social media, and workflow naturally began blending with AI as a central driver of growth. I enrolled in the University of Helsinki’s Elements of AI and then its Ethics of AI courses. Those courses reframed my thinking. AI is not a story of machines replacing people, it is a story of human failure if we do not put governance and ethical structures in place. That perspective pushed me to ask the final question. If organizations and schools are investing billions in AI training, how do we know if it works. How do we measure the value of those programs.

That became the starting point for the Human Enhancement Quotient, or HEQ. I am not presenting HEQ as a finished framework. I am facilitating its development as a measurable way to see how much smarter, faster, and more adaptive people become when they work with AI. It is designed to capture four dimensions: how quickly you connect ideas, how well you make decisions with ethical alignment, how effectively you collaborate, and how fast you grow through feedback. It is a work in progress. That is why I share it openly, because two perspectives are better than one, three are better than two, and every iteration makes it stronger.

The reality is that organizations are already making decisions based on assumptions about who can or cannot thrive in an AI-augmented world. We cannot leave that to guesswork. We need a fair and reliable way to measure human and AI collaborative intelligence. HEQ is one way to start building that foundation, and my hope is that others will join in refining it so that we can reach an ethical solution together.

That is why I made the paper and the work available as a work in progress. In an age where people are losing their jobs because of AI and in a future where everyone seems to claim the title of AI expert, I believe we urgently need a quantitative way to separate assumptions from evidence. Measurement matters because those who position themselves to shape AI will shape the lives and opportunities of others. As I argued in my ethics paper, the real threat to AI is not some science fiction scenario. The real threat is us.

So I am asking for your help. Read the work, test it, challenge it, and improve it. If we can build a standard together, we can create a path that is more ethical, more transparent, and more human-centered.

Full white paper: The Human Enhancement Quotient: Measuring Cognitive Amplification Through AI Collaboration

Open repository for replication: github.com/basilpuglisi/HAIA

References

Accenture. (2025, September 26). Accenture plans on ‘exiting’ staff who can’t be reskilled on AI. CNBC. https://www.cnbc.com/2025/09/26/accenture-plans-on-exiting-staff-who-cant-be-reskilled-on-ai.html
Bloomberg News. (2025, February 2). Microsoft lays off thousands as AI rewrites tech economy. Bloomberg. https://www.bloomberg.com/news/articles/2025-02-02/microsoft-lays-off-thousands-as-ai-rewrites-tech-economy
Carter, N. [@nic__carter]. (2025, April 15). i’ve noticed a weird aversion to using AI on the left… deduct yourself 30+ points of IQ because you don’t like the tech [Post]. X (formerly Twitter). https://x.com/nic__carter/status/1912606269380194657
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
Gawdat, M. (2021, December 3). Mo Gawdat says AI will be smarter than us, so we must teach it to be good now. The Guardian. https://www.theguardian.com/lifeandstyle/2021/dec/03/mo-gawdat-says-ai-will-be-smarter-than-us-so-we-must-teach-it-to-be-good-now
Kasparov, G. (2017). Deep thinking: Where machine intelligence ends and human creativity begins. PublicAffairs.
Puglisi, B. C. (2025). The human enhancement quotient: Measuring cognitive amplification through AI collaboration (v1.0). basilpuglisi.com/HEQ https://basilpuglisi.com/the-human-enhancement-quotient-heq-measuring-cognitive-amplification-through-ai-collaboration-draft
Sarfatti, J. [@JackSarfatti]. (2025, September 26). AI is here to stay. What matters are the prompts put to it… My effective IQ with Super Grok is now 10^3 growing exponentially… [Post]. X (formerly Twitter). https://x.com/JackSarfatti/status/1971705118627373281
University of Helsinki. (n.d.). Elements of AI. https://www.elementsofai.com/
University of Helsinki. (n.d.). Ethics of AI. https://ethics-of-ai.mooc.fi/
World Economic Forum. (2023). Jobs of tomorrow: Large language models and jobs. https://www.weforum.org/reports/jobs-of-tomorrow-large-language-models-and-jobs/

Checkpoint-Based Governance: An Implementation Framework for Accountable Human-AI Collaboration (v2 drafting)

September 23, 2025 by Basil Puglisi Leave a Comment

Executive Summary

Organizations deploying AI systems face a persistent implementation gap: regulatory frameworks and ethical guidelines mandate human oversight, but provide limited operational guidance on how to structure that oversight in practice. This paper introduces Checkpoint-Based Governance (CBG), a protocol-driven framework for human-AI collaboration that operationalizes oversight requirements through systematic decision points, documented arbitration, and continuous accountability mechanisms.

CBG addresses three critical failures in current AI governance approaches: (1) automation bias drift, where humans progressively defer to AI recommendations without critical evaluation; (2) model performance degradation that proceeds undetected until significant harm occurs; and (3) accountability ambiguity when adverse outcomes cannot be traced to specific human decisions.

The framework has been validated across three operational contexts: multi-agent workflow coordination (HAIA-RECCLIN), content quality assurance (HAIA-SMART), and outcome measurement protocols (Factics). Preliminary internal evidence indicates directional improvements in workflow accountability while maintaining complete human decision authority and generating audit-ready documentation for regulatory compliance [PROVISIONAL—internal pilot data].

CBG is designed for risk-proportional deployment, scaling from light oversight for low-stakes applications to comprehensive governance for regulated or brand-critical decisions. This paper presents the theoretical foundation, implementation methodology, and empirical observations from operational deployments.

1. The Accountability Gap in AI Deployment

1.1 Regulatory Requirements Without Implementation Specifications

The regulatory environment for AI systems has matured significantly. The European Union’s Regulation (EU) 2024/1689 (Artificial Intelligence Act) Article 14 mandates “effective human oversight” for high-risk AI systems.¹ The U.S. National Institute of Standards and Technology’s AI Risk Management Framework similarly emphasizes the need for “appropriate methods and metrics to evaluate AI system trustworthiness” and documented accountability structures (NIST, 2023). ISO/IEC 42001:2023, the international standard for AI management systems, codifies requirements for continuous risk assessment, documentation, and human decision authority through structured governance cycles (Bradley, 2025).

However, these frameworks specify outcomes—trustworthiness, accountability, transparency—without prescribing operational mechanisms. Organizations understand they must implement human oversight but lack standardized patterns for structuring decision points, capturing rationale, or preventing the gradual erosion of critical evaluation that characterizes automation bias.

1.2 The Three-Failure Pattern

Operational observation across multiple deployment contexts reveals a consistent pattern of governance failure:

Automation Bias Drift: Human reviewers initially evaluate AI recommendations critically but progressively adopt a default-approve posture as familiarity increases. Research confirms this tendency: automation bias leads to over-reliance on automated recommendations even when those recommendations are demonstrably incorrect (Parasuraman & Manzey, 2010). Without systematic countermeasures, human oversight degrades from active arbitration to passive monitoring.

Model Performance Degradation: AI systems experience concept drift as real-world data distributions shift from training conditions (Lu et al., 2019). Organizations that lack systematic checkpoints often detect performance decay only after significant errors accumulate. The absence of structured evaluation points means degradation proceeds invisibly until threshold failures trigger reactive investigation.

Accountability Ambiguity: When adverse outcomes occur in systems combining human judgment and AI recommendations, responsibility attribution becomes contested. Organizations claim “human-in-the-loop” oversight but cannot produce evidence showing which specific human reviewed the decision, what criteria they applied, or what rationale justified approval. This evidential gap undermines both internal improvement processes and external accountability mechanisms.

1.3 Existing Approaches and Their Limitations

Current governance approaches fall into three categories, each with implementation constraints:

Human-in-the-Loop (HITL) Frameworks: These emphasize human involvement in AI decision processes but often lack specificity about checkpoint placement, evaluation criteria, or documentation requirements. Organizations adopting HITL principles report implementation challenges: 46% cite talent skill gaps and 55% cite transparency issues (McKinsey & Company, 2025). The conceptual framework exists; the operational pattern does not.

Agent-Based Automation: Autonomous agent architectures optimize for efficiency by minimizing human intervention points. While appropriate for well-bounded, low-stakes domains, this approach fundamentally distributes accountability between human boundary-setting and machine execution. When errors occur, determining whether the fault lies in inadequate boundaries or unexpected AI behavior becomes analytically complex.

Compliance Theater: Organizations implement minimal oversight mechanisms designed primarily to satisfy auditors rather than genuinely prevent failures. These systems create documentation without meaningful evaluation, generating audit trails that obscure rather than illuminate decision processes.

The field requires an implementation framework that operationalizes oversight principles with sufficient specificity that organizations can deploy, measure, and continuously improve their governance practices.

Table 1: Comparative Framework Analysis

Dimension	Traditional HITL	Agent Automation	Checkpoint-Based Governance
Accountability Traceability	Variable (depends on implementation)	Distributed (human + machine)	Complete (every decision logged with human rationale)
Decision Authority	Principle: human involvement	AI executes within boundaries	Mandatory human arbitration at checkpoints
Throughput	Variable	Optimized for speed	Constrained by review capacity
Auditability	Often post-hoc	Automated logging of actions	Proactive documentation of decisions and rationale
Drift Mitigation	Not systematically addressed	Requires separate monitoring	Built-in through checkpoint evaluation and approval tracking
Implementation Specificity	Abstract principles	Boundary definition	Defined checkpoint placement, criteria, and logging requirements

2. Checkpoint-Based Governance: Definition and Architecture

2.1 Core Definition

Checkpoint-Based Governance (CBG) is a protocol-driven framework for structuring human-AI collaboration through mandatory decision points where human arbitration occurs, evaluation criteria are systematically applied, and decisions are documented with supporting rationale. CBG functions as a governance layer above AI systems, remaining agent-independent and model-agnostic while enforcing accountability mechanisms.

The framework rests on four architectural principles:

Human Authority Preservation: Humans retain final decision rights at defined checkpoints; AI systems contribute intelligence but do not execute decisions autonomously.
Systematic Evaluation: Decision points apply predefined criteria consistently, preventing ad-hoc judgment and supporting inter-rater reliability.
Documented Arbitration: Every checkpoint decision generates a record including the input evaluated, criteria applied, decision rendered, and human rationale for that decision.
Continuous Monitoring: The framework includes mechanisms for detecting both automation bias drift (humans defaulting to approval) and model performance degradation (AI recommendations declining in quality).

2.2 The CBG Decision Loop

CBG implements a four-stage loop at each checkpoint:

┌─────────────────────────────────────────────────┐

│ Stage 1: AI CONTRIBUTION │

│ AI processes input and generates output │

└────────────────┬────────────────────────────────┘

│

▼

┌─────────────────────────────────────────────────┐

│ Stage 2: CHECKPOINT EVALUATION │

│ Output assessed against predefined criteria │

│ (Automated scoring or structured review) │

└────────────────┬────────────────────────────────┘

│

▼

┌─────────────────────────────────────────────────┐

│ Stage 3: HUMAN ARBITRATION │

│ Designated human reviews evaluation │

│ Applies judgment and contextual knowledge │

│ DECISION: Approve | Modify | Reject | Escalate │

└────────────────┬────────────────────────────────┘

│

▼

┌─────────────────────────────────────────────────┐

│ Stage 4: DECISION LOGGING │

│ Record: timestamp, identifier, decision, │

│ rationale, evaluation results │

│ Output proceeds only after logging completes │

└─────────────────────────────────────────────────┘

This loop distinguishes CBG from autonomous agent architectures (which proceed from AI contribution directly to execution) and from passive monitoring (which lacks mandatory arbitration points).

2.3 Distinction from Related Frameworks

CBG vs. Human-in-the-Loop (HITL): HITL describes the principle that humans should participate in AI decision processes. CBG specifies how: through structured checkpoints with defined evaluation criteria, mandatory arbitration, and logged rationale. HITL is the “what”; CBG is the “how.”

At each CBG checkpoint, the following elements are captured: (1) input under evaluation, (2) criteria applied, (3) evaluation results (scores or qualitative assessment), (4) human decision rendered, (5) documented rationale, (6) timestamp, and (7) reviewer identifier. This operational specificity distinguishes CBG from abstract HITL principles—organizations implementing CBG know precisely what to log and when.

CBG vs. Autonomous Agents: Agents execute decisions within predefined boundaries, optimizing for throughput by minimizing human intervention. CBG inverts this priority: it optimizes for accountability by requiring human arbitration at critical junctures, accepting throughput costs in exchange for traceable responsibility.

CBG vs. Compliance Documentation: Compliance systems often generate audit trails post-hoc or through automated logging without meaningful evaluation. CBG embeds evaluation and arbitration as mandatory prerequisites for decision execution, making documentation a byproduct of genuine oversight rather than a substitute for it.

CBG and Standards Alignment: CBG operationalizes what ISO/IEC 42001 mandates but does not specify. The framework’s decision loop directly implements ISO 42001’s “Govern-Map-Measure-Manage” cycle (Bradley, 2025). CBG also aligns with COBIT control objectives, particularly PO10 (Manage Projects) requirements for documented approvals and accountability chains (ISACA, 2025). Organizations already using these frameworks can map CBG checkpoints to existing control structures without architectural disruption.

3. Implementation Framework

3.1 Risk-Proportional Deployment

CBG recognizes that oversight requirements vary by context. The framework scales across three governance intensities:

Heavy Governance (Comprehensive CBG):

Context: Regulated domains (finance, healthcare, legal), brand-critical communications, high-stakes strategic decisions
Checkpoint Frequency: Every decision point before irreversible actions
Evaluation Method: Multi-criteria assessment with quantitative scoring
Arbitration: Mandatory human review with documented rationale
Monitoring: Continuous drift detection and periodic human-sample audits
Outcome: Complete audit trail suitable for regulatory examination

Moderate Governance (Selective CBG):

Context: Internal knowledge work, customer-facing content with moderate exposure, operational decisions with reversible consequences
Checkpoint Frequency: Key transition points and sample-based review
Evaluation Method: Criteria-based screening with automated flagging of outliers
Arbitration: Human review triggered by flag conditions or periodic sampling
Monitoring: Periodic performance review and spot-checking
Outcome: Balanced efficiency with accountability for significant decisions

Light Governance (Minimal CBG):

Context: Creative exploration, rapid prototyping, low-stakes internal drafts, learning environments
Checkpoint Frequency: Post-deployment review or milestone checks
Evaluation Method: Retrospective assessment against learning objectives
Arbitration: Human review for pattern identification rather than individual approval
Monitoring: Quarterly or project-based evaluation
Outcome: Learning capture with minimal workflow friction

Organizations deploy CBG at the intensity appropriate to risk exposure, scaling up when stakes increase and down when iteration speed matters more than individual decision accountability.

3.2 Implementation Components

Operationalizing CBG requires four foundational components:

Component 1: Decision Rights Matrix

Organizations must specify:

Which roles have checkpoint authority for which decisions
Conditions under which decisions can be overridden
Escalation paths when standard criteria prove insufficient
Override documentation requirements

Example: In a multi-role workflow, the Researcher role has checkpoint authority over source validation, while the Editor role controls narrative approval. Neither can override the other’s domain without documented justification and supervisory approval.

Component 2: Evaluation Criteria Specification

Each checkpoint requires defined evaluation criteria:

Quantitative metrics where possible (scoring thresholds)
Qualitative standards with examples (what constitutes acceptable quality)
Boundary conditions (when to automatically reject or escalate)
Calibration mechanisms (inter-rater reliability checks)

Example: Content checkpoints might score hook strength (1-10), competitive differentiation (1-10), voice consistency (1-10), and CTA clarity (1-10), with documented examples of scores at each level.

Component 3: Logging Infrastructure

Decision records must capture:

Input evaluated (what was assessed)
Criteria applied (what standards were used)
Evaluation results (scores or qualitative assessment)
Decision rendered (approve/modify/reject/escalate)
Human identifier (who made the decision)
Rationale (why this decision was appropriate)
Timestamp (when the decision occurred)

This generates an audit trail suitable for both internal learning and external compliance demonstration.

Component 4: Drift Detection Mechanisms

Automated monitoring should track:

Approval rate trends: Increasing approval rates may indicate automation bias
Evaluation score distributions: Narrowing distributions suggest criteria losing discriminatory power
Time-to-decision patterns: Decreasing review time may indicate cursory evaluation
Decision reversal frequency: Low reversal rates across multiple reviewers suggest insufficient critical engagement
Model performance metrics: Comparing AI recommendation quality to historical baselines

When drift indicators exceed thresholds, the system triggers human investigation and potential checkpoint recalibration.

4. Operational Implementations

4.1 HAIA-RECCLIN: Role-Based Collaboration Governance

Context: Multi-person, multi-AI workflows requiring coordinated contributions across specialized domains.

Implementation: Seven roles (Researcher, Editor, Coder, Calculator, Liaison, Ideator, Navigator), each with checkpoint authority for their domain. Work products transition between roles only after checkpoint approval. Each role applies domain-specific evaluation criteria and documents arbitration rationale.

Figure 1: RECCLIN Role-Based Checkpoint Flow

NAVIGATOR

[Scope Definition] → CHECKPOINT → [Approve Scope]

│

▼

RESEARCHER

[Source Validation] → CHECKPOINT → [Approve Sources]

│

▼

EDITOR

[Narrative Review] → CHECKPOINT → [Approve Draft]

│

▼

CALCULATOR

[Verify Numbers] → CHECKPOINT → [Certify Data]

│

▼

CODER

[Code Review] → CHECKPOINT → [Approve Implementation]

Each checkpoint requires: Evaluation + Human Decision + Logged Rationale

Checkpoint Structure:

Navigator defines project scope and success criteria (checkpoint: boundary validation)
Researcher validates information sources (checkpoint: source quality and bias assessment)
Editor ensures narrative coherence (checkpoint: clarity and logical flow)
Calculator verifies quantitative claims (checkpoint: methodology and statistical validity)
Coder reviews technical implementations (checkpoint: security, efficiency, maintainability)
Ideator evaluates innovation proposals (checkpoint: feasibility and originality)
Liaison coordinates stakeholder communications (checkpoint: appropriateness and timing)

Each role has equal checkpoint authority within their domain. Navigator does not override Calculator on mathematical accuracy; Calculator does not override Editor on narrative tone. Cross-domain overrides require documented justification and supervisory approval.

Observed Outcomes: Role-based checkpoints reduce ambiguity about decision authority and create clear accountability chains. Conflicts between roles are documented rather than resolved through informal negotiation, generating institutional knowledge about evaluation trade-offs.

4.2 HAIA-SMART: Content Quality Assurance

Context: AI-generated content requiring brand voice consistency and strategic messaging alignment.

Implementation: Four-criteria evaluation (hook strength, competitive differentiation, voice consistency, call-to-action clarity). AI drafts receive automated scoring, human reviews scores and content, decision (publish/edit/reject) is logged with rationale.

Checkpoint Structure:

AI generates draft content
Automated evaluation scores against four criteria (0-10 scale)
Human reviews scores and reads content
Human decides: publish as-is, edit and re-evaluate, or reject
Decision logged with specific rationale (e.g., “voice inconsistent despite acceptable score—phrasing too formal for audience”)

Observed Outcomes (6-month operational data):

100% human approval rate (zero autonomous publications)
Zero published content requiring subsequent retraction [PROVISIONAL—internal operational data, see Appendix A]
Preliminary internal evidence indicates directional improvements in engagement metrics [PROVISIONAL—internal pilot data]
Complete audit trail for brand governance compliance

Key Learning: Automated scoring provides useful signal but cannot replace human judgment for nuanced voice consistency evaluation. The checkpoint prevented several high-scoring drafts from publication because human review detected subtle brand misalignments that quantitative metrics missed.

4.3 Factics: Outcome Measurement Protocol

Context: Organizational communications requiring outcome accountability and evidence-based claims.

Implementation: Every factual claim must be paired with a defined tactic (how the fact will be used) and a measurable KPI (how success will be determined). Claims cannot proceed to publication without passing the Factics checkpoint.

Checkpoint Structure:

Claim proposed: “CBG improves accountability”
Tactic defined: “Implement CBG in three operational contexts”
KPI specified: “Measure audit trail completeness (target: 100% decision documentation), time-to-arbitration (target: <24 hours), decision reversal rate (target: <5%)”
Human validates that claim-tactic-KPI triad is coherent and measurable
Decision logged: approve for development, modify for clarity, reject as unmeasurable

Observed Outcomes: Factics checkpoints eliminate aspirational claims without evidence plans. The discipline of pairing claims with measurement criteria prevents common organizational dysfunction where stated objectives lack implementation specificity.

5. Comparative Analysis

5.1 CBG vs. Traditional HITL Implementations

Traditional HITL approaches emphasize human presence in decision loops but often lack operational specificity. Research confirms adoption challenges: organizations report difficulty translating HITL principles into systematic workflows, with 46% citing talent skill gaps and 55% citing transparency issues as primary barriers (McKinsey & Company, 2025).

CBG’s Operational Advantage: By specifying checkpoint placement, evaluation criteria, and documentation requirements, CBG provides implementable patterns. Organizations can adopt CBG with clear understanding of required infrastructure (logging systems, criteria definition, role assignment) rather than struggling to operationalize abstract oversight principles.

Empirical Support: Studies show momentum toward widespread governance adoption, with projected risk reductions through structured, human-led approaches (ITU, 2025). CBG’s systematic approach aligns with this finding: explicitly defined checkpoints outperform ad-hoc oversight.

5.2 CBG vs. Agent-Based Automation

Autonomous agents optimize for efficiency by minimizing human bottlenecks. For well-defined, low-risk tasks, this architecture delivers significant productivity gains. However, for high-stakes or nuanced decisions, agent architectures distribute accountability in ways that complicate error attribution.

CBG’s Accountability Advantage: By requiring human arbitration at decision points, CBG ensures that when outcomes warrant investigation, a specific human made the call and documented their reasoning. This trades some efficiency for complete traceability.

Use Case Differentiation: Organizations should deploy agents for high-volume, low-stakes tasks with clear success criteria (e.g., routine data processing, simple customer inquiries). They should deploy CBG for consequential decisions where accountability matters (e.g., credit approvals, medical triage, brand communications).

Contrasting Case Study: Not all contexts require comprehensive CBG. Visa’s Trusted Agent Protocol (2025) demonstrates successful limited-checkpoint deployment in a narrowly-scoped domain: automated transaction verification within predefined risk boundaries. This agent architecture succeeds because the operational envelope is precisely bounded, error consequences are financially capped, and monitoring occurs continuously. In contrast, domains with evolving criteria, high-consequence failures, or regulatory accountability requirements—such as credit decisioning, medical diagnosis, or brand communications—justify CBG’s more intensive oversight. The framework choice should match risk profile.

5.3 CBG Implementation Costs

Organizations considering CBG adoption should anticipate three cost categories:

Setup Costs:

Defining decision rights matrices
Specifying evaluation criteria
Implementing logging infrastructure
Training humans on checkpoint protocols

Operational Costs:

Time for human arbitration at checkpoints
Periodic criteria calibration and drift detection
Audit trail storage and retrieval systems

Opportunity Costs:

Reduced throughput compared to fully automated approaches
Delayed decisions when checkpoint queues develop

Return on Investment: These costs are justified when error consequences exceed operational overhead. Organizations in regulated industries, those with brand-critical communications, or contexts where single failures create significant harm will find CBG’s accountability benefits worth the implementation burden.

6. Limitations and Constraints

6.1 Known Implementation Challenges

Challenge 1: Automation Bias Still Occurs

Despite systematic checkpoints, human reviewers can still develop approval defaults. CBG mitigates but does not eliminate this risk. Recent evidence confirms automation bias persists across domains, with reviewers showing elevated approval rates after extended exposure to consistent AI recommendations (Parasuraman & Manzey, 2010). Countermeasures include:

Periodic rotation of checkpoint responsibilities
Second-reviewer sampling to detect approval patterns
Automated flagging when approval rates exceed historical norms

Challenge 2: Checkpoint Fatigue

High-frequency checkpoints can lead to reviewers experiencing evaluation fatigue, reducing decision quality. Organizations must calibrate checkpoint density to human capacity and consider batch processing or asynchronous review to prevent overload.

Challenge 3: Criteria Gaming

When evaluation criteria become well-known, AI systems or human contributors may optimize specifically for those criteria rather than underlying quality. This requires periodic criteria evolution to prevent metric fixation.

6.2 Contexts Where CBG Is Inappropriate

CBG is not suitable for:

Rapid prototyping environments where learning from failure is more valuable than preventing individual errors
Well-bounded, high-volume tasks where agent automation delivers clear efficiency gains without accountability concerns
Creative exploration where evaluation criteria would constrain beneficial experimentation

Organizations should match governance intensity to risk profile rather than applying uniform oversight across all AI deployments.

6.3 Measurement Limitations

Current CBG implementations rely primarily on process metrics (checkpoint completion rates, logging completeness) rather than outcome metrics (decisions prevented errors in X% of cases). This limitation reflects the difficulty of counterfactual analysis: determining what would have happened without checkpoints.

Future research should focus on developing methods to quantify CBG’s error-prevention effectiveness through controlled comparison studies.

6.4 Responding to Implementation Critiques

Critique 1: Governance Latency

Critics argue that checkpoint-based governance impedes agility by adding human review time to decision cycles (Splunk, 2025). This concern is valid but addressable through risk-proportional deployment. Organizations can implement light governance for low-stakes rapid iteration while reserving comprehensive checkpoints for consequential decisions. The latency cost is intentional: it trades speed for accountability where stakes justify that trade.

Critique 2: Compliance Theater Risk

Documentation-heavy governance can devolve into “compliance theater,” where organizations generate audit trails without meaningful evaluation (Precisely, 2025). CBG mitigates this risk by embedding rationale capture as a mandatory component of arbitration. The checkpoint cannot be satisfied with a logged decision alone; the human reviewer must document why that decision was appropriate. This transforms documentation from bureaucratic burden to institutional learning.

Critique 3: Human Variability

Checkpoint effectiveness depends on consistent human judgment, but reviewers introduce variability and experience fatigue (Parasuraman & Manzey, 2010). CBG addresses this through reviewer rotation, periodic calibration exercises, and automated flagging when approval patterns deviate from historical norms. These countermeasures reduce but do not eliminate human-factor risks.

Critique 4: Agent Architecture Tension

Self-correcting autonomous agents may clash with protocol-driven checkpoints (Nexastack, 2025). However, CBG’s model-agnostic design allows integration: agent self-corrections become Stage 2 evaluations in the CBG loop, with human arbitration preserved for consequential decisions. This enables organizations to leverage agent capabilities while maintaining accountability architecture.

7. Future Research Directions

7.1 Quantitative Effectiveness Studies

Rigorous CBG evaluation requires controlled studies comparing outcomes under three conditions:

Autonomous AI decision-making (no human checkpoints)
Unstructured human oversight (HITL without CBG protocols)
Structured CBG implementation

Outcome measures should include error rates, decision quality scores, audit trail completeness, and time-to-decision metrics.

7.2 Cross-Domain Portability

Current implementations focus on collaboration workflows, content generation, and measurement protocols. Research should explore CBG application in additional domains:

Financial lending decisions
Healthcare diagnostic support
Legal document review
Security access approvals
Supply chain optimization

Checkpoint-based governance has analogues in other high-reliability domains. The Federal Aviation Administration’s standardized checklists exemplify systematic checkpoint architectures that prevent errors in high-stakes contexts. Aviation’s “challenge-response” protocols—where one crew member verifies another’s actions—mirror CBG’s arbitration requirements. These proven patterns demonstrate that structured checkpoints enhance rather than impede performance when consequences are significant.

Comparative analysis across domains would identify core CBG patterns versus domain-specific adaptations.

7.3 Integration with Emerging AI Architectures

As AI systems evolve toward more sophisticated reasoning and multi-step planning, CBG checkpoint placement may require revision. Research should investigate:

Optimal checkpoint frequency for chain-of-thought reasoning systems
How to apply CBG to distributed multi-agent AI systems
Checkpoint design for AI systems with internal self-correction mechanisms

7.4 Standardization Efforts

CBG’s practical value would increase significantly if standardized implementation templates existed for common use cases. Collaboration with standards bodies (IEEE, ISO/IEC 42001, NIST) could produce:

Reference architectures for CBG deployment
Evaluation criteria libraries for frequent use cases
Logging format standards for cross-organizational comparability
Audit protocols for verifying CBG implementation fidelity

8. Recommendations

8.1 For Organizations Deploying AI Systems

Conduct risk assessment to identify high-stakes decisions requiring comprehensive oversight
Implement CBG incrementally, starting with highest-risk applications
Invest in logging infrastructure before scaling checkpoint deployment
Define evaluation criteria explicitly with concrete examples at each quality level
Monitor for automation bias through periodic sampling and approval rate tracking
Plan for iteration: initial checkpoint designs will require refinement based on operational experience

8.2 For Regulatory Bodies

Recognize operational diversity in oversight implementation; specify outcomes (documented decisions, human authority) rather than mandating specific architectures
Require audit trail standards that enable verification without prescribing logging formats
Support research into governance effectiveness measurement to build evidence base
Encourage industry collaboration on checkpoint pattern libraries for common use cases

8.3 For Researchers

Prioritize comparative effectiveness studies with rigorous experimental controls
Develop outcome metrics beyond process compliance (e.g., error prevention rates)
Investigate human factors in checkpoint fatigue and automation bias
Explore cross-domain portability to identify universal vs. context-specific patterns

9. Conclusion

Checkpoint-Based Governance addresses the implementation gap between regulatory requirements for human oversight and operational reality in AI deployments. By specifying structured decision points, systematic evaluation criteria, mandatory human arbitration, and comprehensive documentation, CBG operationalizes accountability in ways that abstract HITL principles cannot.

The framework is not a panacea. It imposes operational costs, requires organizational discipline, and works best when matched to appropriate use cases. However, for organizations deploying AI in high-stakes contexts where accountability matters—regulated industries, brand-critical communications, consequential decisions affecting individuals—CBG provides a tested pattern for maintaining human authority while leveraging AI capabilities.

Three operational implementations demonstrate CBG’s portability across domains: collaboration workflows (HAIA-RECCLIN), content quality assurance (HAIA-SMART), and outcome measurement (Factics). Preliminary internal evidence indicates directional improvements in workflow accountability alongside complete decision traceability [PROVISIONAL—internal pilot data].

The field needs continued research, particularly controlled effectiveness studies and cross-domain validation. Organizations implementing CBG should expect to iterate on checkpoint designs based on operational learning. Regulatory bodies can support adoption by recognizing diverse implementation approaches while maintaining consistent outcome expectations.

Checkpoint-Based Governance represents a pragmatic synthesis of governance principles and operational requirements. It is evolutionary rather than revolutionary—building on HITL research, design pattern theory, ISO/IEC 42001 management system standards, and risk management frameworks. Its value lies in implementation specificity: organizations adopting CBG know what to build, how to measure it, and how to improve it over time.

For the AI governance community, CBG offers a vocabulary and pattern library for the accountability architecture that regulations demand but do not specify. That operational clarity is what organizations need most.

Appendix A: Sample Checkpoint Log Entry

CHECKPOINT LOG ENTRY – REDACTED EXAMPLE

Checkpoint Type: Content Quality (HAIA-SMART)

Timestamp: 2024-10-08T14:23:17Z

Reviewer ID: [REDACTED]

Input Document: draft_linkedin_post_20241008.md

Evaluation Results:

– Hook Strength: 8/10 (Strong opening question)

– Competitive Differentiation: 7/10 (Unique angle on governance)

– Voice Consistency: 6/10 (Slightly too formal for usual tone)

– CTA Clarity: 9/10 (Clear next action)

Human Decision: MODIFY

Rationale: “Voice score indicates formality drift. The phrase

‘organizations must implement’ should be softened to ‘organizations

should consider.’ Competitive differentiation is adequate but could

be strengthened by adding specific example in paragraph 3. Hook and

CTA are publication-ready.”

Next Action: Edit draft per rationale, re-evaluate

Status: Pending revision

Appendix B: CBG Mapping to Established Standards

CBG Component	ISO/IEC 42001	NIST AI RMF	COBIT	EU AI Act
Decision Rights Matrix	§6.1 Risk Management, §7.2 Roles	Govern 1.1 (Accountability)	PO10 (Manage Projects)	Article 14(4)(a) Authority
Evaluation Criteria	§8.2 Performance, §9.1 Monitoring	Measure 2.1 (Evaluation)	APO11 (Quality Management)	Article 14(4)(b) Understanding
Human Arbitration	§5.3 Organizational Roles	Manage 4.1 (Incidents)	APO01 (Governance Framework)	Article 14(4)(c) Oversight Capability
Decision Logging	§7.5 Documentation, §9.2 Analysis	Govern 1.3 (Transparency)	MEA01 (Performance Management)	Article 14(4)(d) Override Authority
Drift Detection	§9.3 Continual Improvement	Measure 2.7 (Monitoring)	BAI03 (Solutions Management)	Article 61 (Post-Market Monitoring)

This table demonstrates how CBG operationalizes requirements across multiple governance frameworks, facilitating adoption by organizations already committed to these standards.

References

Bradley, A. (2025). Global AI governance: Five key frameworks explained. Bradley Law Insights. https://www.bradley.com/insights/publications/2025/08/global-ai-governance-five-key-frameworks-explained
Congruity 360. (2025, June 23). Building your AI data governance framework. https://www.congruity360.com/blog/building-your-ai-data-governance-framework
European Parliament and Council. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). Official Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
ISACA. (2025, February 3). COBIT: A practical guide for AI governance. https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2025/cobit-a-practical-guide-for-ai-governance
International Telecommunication Union. (2025). The annual AI governance report 2025: Steering the future of AI. ITU Publications. https://www.itu.int/epublications/publication/the-annual-ai-governance-report-2025-steering-the-future-of-ai
Lumenalta. (2025, March 3). AI governance checklist (Updated 2025). https://lumenalta.com/insights/ai-governance-checklist-updated-2025
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2019). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2346–2363. https://doi.org/10.1109/TKDE.2018.2876857
McKinsey & Company. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
Nexastack. (2025). Agent governance at scale. https://www.nexastack.ai/blog/agent-governance-at-scale
OECD. (2025). Steering AI’s future: Strategies for anticipatory governance. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/steering-ai-s-future_70e4a856/5480ff0a-en.pdf
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
Precisely. (2025, August 11). AI governance frameworks: Cutting through the chaos. https://www.precisely.com/datagovernance/opening-the-black-box-building-transparent-ai-governance-frameworks
Splunk. (2025, February 25). AI governance in 2025: A full perspective. https://www.splunk.com/en_us/blog/learn/ai-governance.html
Stanford Institute for Human-Centered Artificial Intelligence. (2024). Artificial Intelligence Index Report 2024. Stanford University. https://aiindex.stanford.edu/report/
Strobes Security. (2025, July 1). AI governance framework for security leaders. https://strobes.co/blog/ai-governance-framework-for-security-leaders
Superblocks. (2025, July 31). What is AI model governance? https://www.superblocks.com/blog/ai-model-governance
Visa. (2025). Visa introduces Trusted Agent Protocol: An ecosystem-led framework for AI commerce. https://investor.visa.com/news/news-details/2025/Visa-Introduces-Trusted-Agent-Protocol

Footnotes

About the Author: Human-AI Collaboration Strategist specializing in governance frameworks for enterprise AI transformation. Developer of HAIA-RECCLIN, HAIA-SMART, Factics, and the Checkpoint-Based Governance framework. Advisor to organizations implementing accountable AI systems in regulated contexts.

Contact: Basil C Puglisi, basil@puglisiconsulting.com, via basilpuglisi.com

Acknowledgments: This position paper builds on operational experience deploying CBG across multiple organizational contexts and benefits from validation feedback from multiple AI systems and practitioners in AI governance, enterprise architecture, and regulatory compliance domains. Version 2.0 incorporates multi-source validation including conceptual, structural, and technical review.

From Metrics to Meaning: Building the Factics Intelligence Dashboard

August 6, 2025 by Basil Puglisi 2 Comments

The idea of intelligence has always fascinated me. For more than a century, people have tried to measure it through numbers and tests that promise to define potential. IQ became the shorthand for brilliance, but it never captured how people actually perform in complex, changing environments. It measured what could be recalled, not what could be realized.

That tension grew sharper when artificial intelligence entered the picture. The online conversation around AI and IQ had become impossible to ignore. Garry Kasparov, the chess grandmaster who once faced Deep Blue, wrote in Deep Thinking that the real future of intelligence lies in partnership. His argument was clear: humans working with AI outperform both human experts and machines acting alone (Kasparov, 2017). In his Harvard Business Review essays, he reinforced that collaboration, not competition, would define the next leap in intelligence.

By mid-2025, the debate had turned practical. Nic Carter, a venture capitalist, posted that rejecting AI was like ‘deducting 30 IQ points’ from yourself. Mo Gawdat, a former Google X executive, went further on August 4, saying that using AI was like ‘borrowing 50 IQ points,’ which made natural intelligence differences almost irrelevant. Whether those numbers were literal or not did not matter. What mattered was the pattern. People were finally recognizing that intelligence was no longer a fixed human attribute. It was becoming a shared system.

That realization pushed me to find a way to measure it. I wanted to understand how human intelligence behaves when it works alongside machine intelligence. The goal was not to test IQ, but to track how thinking itself evolves when supported by artificial systems. That question became the foundation for the Factics Intelligence Dashboard.

The inspiration for measurement came from the same place Kasparov drew his insight: chess. The early human-machine matches revealed something profound. When humans played against computers, the machine often won. But when humans worked with computers, they dominated both human-only and machine-only teams. The reason was not speed or memory, it was collaboration. The computer calculated the possibilities, but the human decided which ones mattered. The strength of intelligence came from connection.

The Factics Intelligence Dashboard (FID) was designed to measure that connection. I wanted a model that could track not just cognitive skill, but adaptive capability. IQ was built to measure intelligence in isolation. FID would measure it in context.

The model’s theoretical structure came from the thinkers who had already challenged IQ’s limits. Howard Gardner proved that intelligence is not singular but multiple, encompassing linguistic, logical, interpersonal, and creative dimensions (Gardner, 1983). Robert Sternberg built on that with his triarchic theory, showing that analytical, creative, and practical intelligence all contribute to human performance (Sternberg, 1985).

Carol Dweck’s work reframed intelligence as a capacity that grows through challenge (Dweck, 2006). That research became the basis for FID’s Adaptive Learning domain, which measures how efficiently someone absorbs new tools and integrates change. Daniel Goleman expanded the idea further by proving that emotional and social intelligence directly influence leadership, collaboration, and ethical decision-making (Goleman, 1995).

Finally, Brynjolfsson and McAfee’s analysis of human-machine collaboration in The Second Machine Age confirmed that technology does not replace intelligence, it amplifies it (Brynjolfsson & McAfee, 2014).

From these foundations, FID emerged with six measurable domains that define applied intelligence in action:

Verbal / Linguistic measures clarity, adaptability, and persuasion in communication.
Analytical / Logical measures reasoning, structure, and accuracy in solving problems.
Creative measures originality that produces usable innovation.
Strategic measures foresight, systems thinking, and long-term alignment.
Emotional / Social measures empathy, awareness, and the ability to lead or collaborate.
Adaptive Learning measures how fast and effectively a person learns, integrates, and applies new knowledge or tools.

When I began testing FID across both human and AI examples, the contrast was clear. Machines were extraordinary in speed and precision, but they lacked empathy and the subtle decision-making that comes from experience. Humans showed depth and discernment, but they became exponentially stronger when paired with AI tools. Intelligence was no longer static, it was interactive.

The Factics Intelligence Dashboard became a mirror for that interaction. It showed how intelligence performs, not in theory but in practice. It measured clarity, adaptability, empathy, and foresight as the real currencies of intelligence. IQ was never replaced, it was redefined through connection.

Appendix: The Factics Intelligence Dashboard Prompt

Title: Generate an AI-Enhanced Factics Intelligence Dashboard

Instructions: Build a six-domain intelligence profile using the Factics Intelligence Dashboard (FID) model.

The six domains are:

1. Verbal / Linguistic: clarity, adaptability, and persuasion in communication.

2. Analytical / Logical: reasoning, structure, and problem-solving accuracy.

3. Creative: originality, ideation, and practical innovation.

4. Strategic: foresight, goal alignment, and systems thinking.

5. Emotional / Social: empathy, leadership, and audience awareness.

6. Adaptive Learning: ability to integrate new tools, data, and systems efficiently.

Assign a numeric score between 0 and 100 to each domain reflecting observed or modeled performance.

Provide a one-sentence insight statement per domain linking skill to real-world application.

Summarize findings in a concise Composite Insight paragraph interpreting overall cognitive balance and professional strengths.

Keep tone consultant grade, present tense, professional, and data oriented.

Add footer: @BasilPuglisi – Factics Consulting | #AIgenerated

Output format: formatted text or table suitable for PDF rendering or dashboard integration.

References

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
Carter, N. [@nic__carter]. (2025, April 15). I’ve noticed a weird aversion to using AI… it seems like a massive self-own to deduct yourself 30+ points of IQ because you don’t like the tech [Post]. X. https://twitter.com/nic__carter/status/1780330420201979904
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
Gawdat, M. [@mgawdat]. (2025, August 4). Using AI is like ‘borrowing 50 IQ points’ [Post]. X. https://www.tekedia.com/former-google-executive-mo-gawdat-warns-ai-will-replace-everyone-even-ceos-and-podcasters/
Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. Bantam Books.
Kasparov, G. (2017). Deep thinking: Where machine intelligence ends and human creativity begins. PublicAffairs.
Kasparov, G. (2021, March). How to build trust in artificial intelligence. Harvard Business Review. https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.