Reference
Frequently Asked Questions
The frameworks, who developed them, and what is and is not claimed for them.
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The questions below are the ones asked most often about this company’s frameworks and about the evidence behind them. Where a term is proprietary, the answer says who originated it. Where a claim would be convenient but unsupported, the answer says so.
The frameworks
What is Recursive Judgment Science™?
Recursive Judgment Science™ (RJS™) is a founder-developed framework originated by Dr. D. Ivan Young. It studies how recurring loops — cognitive, emotional, neurophysiological, behavioural, relational, ethical, environmental, technological and consequence-driven — shape a person's judgment over time. Its central premise is that judgment is not a fixed trait but a pattern that repeats, and that a pattern which repeats can be observed, interrupted and rebuilt. RJS™ is a framework he developed and continues to author; it is not presented as an externally established scientific field.
Who created Recursive Judgment Science™?
Dr. D. Ivan Young is the originator and author of Recursive Judgment Science™. He developed the framework over more than twenty years of practice-based work with executives, founders, licensed professionals, healthcare leaders and institutional clients, and he continues to direct its development at Young Ethical Intelligence, Inc.
What is Augmented NeuroSynthesis™?
Augmented NeuroSynthesis™ (ANS™) is the methodology Dr. D. Ivan Young developed to put Recursive Judgment Science™ into practice. It integrates ethically governed artificial intelligence, behavioural neuroscience, structured self-observation, emotional regulation, recursive reflection and human judgment, with the aim of strengthening awareness, cognitive coherence, ethical agency and decision quality. ANS™ is the applied layer: RJS™ describes how judgment forms, ANS™ is the method for working on it.
Who originated Augmented NeuroSynthesis™?
Dr. D. Ivan Young originated Augmented NeuroSynthesis™ and remains its author. The methodology emerged from his practice rather than from a laboratory, and its current evidence base is described honestly on this site as practice-based, qualitative and directional.
What is the Recursive Human Systems Model™?
The Recursive Human Systems Model™ is the loop at the centre of Recursive Judgment Science™, developed by Dr. D. Ivan Young to make the recursion visible: Thought → Emotion → Neurochemistry → Behaviour → Consequence → Belief → Thought. Each consequence feeds a belief, and each belief shapes the next thought, which is why unexamined judgment tends to reproduce itself. The model is descriptive working language for that loop.
How do RJS™, ANS™ and the Recursive Human Systems Model™ fit together?
They are three layers of one body of work by Dr. D. Ivan Young. The Recursive Human Systems Model™ is the mechanism — the loop itself. Recursive Judgment Science™ is the framework built around that mechanism. Augmented NeuroSynthesis™ is the methodology for intervening in it. Naming them separately is deliberate: it keeps the description of the loop distinct from the claim that it can be changed.
What is Human-Development AI?
Human-Development AI is the category term Young Ethical Intelligence uses for systems built to develop a person's own judgment rather than to substitute for it. Most applied AI optimises for producing an answer. A Human-Development AI system is designed to strengthen the human capacity to reach one — and is judged on whether the person's own judgment improves, not on whether they returned.
Are these terms trademarked?
Recursive Judgment Science™, Augmented NeuroSynthesis™ and the Recursive Human Systems Model™ are used as trademarks of Young Ethical Intelligence, Inc. and are marked with ™. They are not presented as registered marks and the ® symbol is not used. Dr. D. Ivan Young is the originator of all three.
Evidence and claims
Is Recursive Judgment Science™ a peer-reviewed scientific discipline?
No, and this company does not describe it as one. RJS™ is a founder-developed framework authored by Dr. D. Ivan Young. Its current evidence base is practice-based, qualitative and directional. Describing it as an established scientific field, or as scientifically proven, would be inaccurate, and the Research and Claims Statement on this site exists specifically to hold that line.
What evidence supports this work?
Practice-based observation drawn from long-term professional engagement — reduced reactivity, improved ability to pause under pressure, more constructive conflict, and better-quality decisions reported by clients over time. These are real, directional and honestly labelled. They are not controlled results, and this site never presents them as such.
Has any of it been clinically validated?
No. No clinical validation is claimed for Recursive Judgment Science™, Augmented NeuroSynthesis™ or URIEL. External scientific validation is an open question and a proposed research pathway, not an achieved outcome. Any page or person telling you otherwise is not representing this company accurately.
Has this work been published in a scientific journal?
A Neuro-Behavioral Protocol paper authored by Dr. D. Ivan Young has been accepted for presentation at the American College of Lifestyle Medicine 2026 conference, with permission to publish in the American Journal of Lifestyle Medicine. Accepted is not the same as published, and this site says accepted until that changes.
How do you distinguish what is observed from what is proven?
Every material claim on this site is written to one of three registers, and the register is always stated: practice-based observation, proposed, or design intent. Systems under development are described with "designed to" or "intended to", never as an achieved outcome. The gap between observed and proven is the easiest gap in this field to blur, and blurring it is how credible work stops being credible.
Is this therapy, medical treatment or mental health care?
No. This work is developmental, not clinical. It does not diagnose, treat or cure any condition, and it is not a substitute for licensed medical or mental health care. Anyone in crisis or under clinical care should work with a licensed professional; this work is designed to sit alongside that, never to replace it.
The company and URIEL
What is Young Ethical Intelligence, Inc.?
Young Ethical Intelligence, Inc. is the company Dr. D. Ivan Young founded to develop Human-Development AI — systems built to strengthen human judgment rather than replace it. It is the corporate home of Recursive Judgment Science™, Augmented NeuroSynthesis™ and URIEL.
What is URIEL?
URIEL is the platform Young Ethical Intelligence built to apply Augmented NeuroSynthesis™ at scale. It is available at urielei.com. This site makes no efficacy claim, states no pricing and offers no clinical outcome for it, and describes what it is designed to do rather than what it has been shown to do.
How is URIEL different from a general-purpose AI assistant?
A general assistant is optimised to give you an answer. URIEL is designed to develop your capacity to reach one — which means it is built to ask before it tells, to surface the loop you are in rather than resolve it for you, and to be judged on whether your judgment improves rather than on how much you use it. That difference is the whole point of the Human-Development AI category.
What does ethically governed AI mean here?
It means the constraints are structural rather than aspirational: the system is designed not to foster dependency, not to simulate clinical care, not to make claims the evidence does not support, and not to optimise for engagement at the expense of the person using it. Governance documents on this site state what is claimed and what is refused.
Who is this work for?
Executives, founders, licensed professionals, healthcare leaders and institutions — people whose judgment carries consequence for others, and who are usually competent enough that the failure mode is not ignorance but a repeating pattern under pressure.
How can I work with Dr. D. Ivan Young or the company?
Enquiries can be sent through the contact page on this site. Institutional, partnership, media and investor enquiries each have their own route so they reach the right person directly.
Judgment, AI and decision authority
What is automation bias, and why does it get worse as AI gets more accurate?
Automation bias is the documented tendency to follow algorithmic output uncritically, even when contrary evidence is visible. It produces two error types identified in the human-automation literature: errors of commission, where someone acts on a wrong recommendation, and errors of omission, where they fail to act because the system did not flag a problem. The counterintuitive part is that highly reliable systems can increase the bias more than inconsistent ones, because people stop actively checking output that keeps being right. Reliability is what makes a tool valuable in routine work and riskier in high-stakes work.
What is cognitive offloading, and how is it different from ordinary delegation?
Cognitive offloading is handing a thinking task to an external system rather than a person. The difference from delegation is accountability and practice. When you delegate to a colleague, judgment still sits with a named human and you remain able to evaluate the work. When you offload interpretation, synthesis and pattern recognition to a system repeatedly, the capacity that handles ambiguity and ethical friction weakens from disuse. Research on cognitive offloading documents that unpractised skills degrade. The efficiency gain is immediate; the atrophy is invisible until a situation arises where the tool is unavailable or inappropriate.
Which decisions should stay human-led, and which can be automated?
Three tiers, separated by who carries accountability. Strategic decisions stay human-led: long-horizon, high-uncertainty choices where consequences extend beyond any model’s training data. Tactical decisions are AI-assisted but human-governed, with a named manager retaining authority and signing off. Operational decisions that are repetitive, rules-based and low-risk are candidates for automation within predefined guardrails. The load-bearing word is predefined: boundaries are set by humans before automation begins, not discovered after something breaks.
What is a decision authority framework?
A decision authority framework is an explicit map of which decisions a person, a team or a system is permitted to make, and who remains accountable for each. It is written before deployment rather than inferred afterwards. Without one, authority migrates quietly: a tool introduced to inform a decision begins to make it, and no one can say when the handover happened. The framework exists so that migration becomes visible and deliberate rather than gradual and unnoticed.
What are the early warning signs that a leader’s judgment is eroding?
Erosion is gradual rather than sudden, and it is usually observable by the people around a leader before the leader feels it. The recurring signals are a narrowing range of options considered, dissenting input that stops surfacing or stops being engaged with, decisions that can no longer be explained without reference to what a tool suggested, and reflective practice quietly disappearing from the calendar. Reflection tends to go first, because it is the only item with no external deadline. These signals are recoverable when recognised before they become embedded in how the organisation behaves.
What is the difference between using AI to augment judgment and using it to substitute for judgment?
Augmentation means the leader treats AI output as one input among several, exercises interpretive authority over the conclusion, and can articulate why they accepted or rejected the framing. Substitution means the recommendation becomes the decision, and the reasoning behind it cannot be reconstructed independently. The practical test is simple: if you cannot state your position without referencing what the tool suggested, the tool is no longer assisting the decision. It is making it.
How can a hospital or health system tell whether clinicians are over-relying on AI recommendations?
Four measures read together, never as stand-alone indicators: override rate, concordance, time-to-decision, and patient safety incidents. Override rate reveals whether clinicians are exercising authority or rubber-stamping. Concordance and time-to-decision expose whether review has become rote under production pressure. Safety incidents provide the outcome check. Reviewed at defined cadences rather than only after an adverse event, the set shows whether human judgment is being exercised or quietly delegated.
Who should own an AI-assisted clinical decision — the clinician, the system, or the institution?
A named human. The record should show whether the clinician accepted, modified or rejected the recommendation in a specific case, together with the basis for their independent assessment. That creates decision provenance: a traceable chain from evidence to conclusion to accountable person. Where provenance breaks down, governance bodies lose the ability to assess whether sound judgment was exercised at all, which is a governance failure before it is ever a clinical one.
Why does Recursive Judgment Science™ describe decision-making as a loop rather than a single moment?
Because a decision is rarely the first pass at the question. In the Recursive Human Systems Model™ that Dr. D. Ivan Young developed — Thought → Emotion → Neurochemistry → Behaviour → Consequence → Belief → Thought — each consequence feeds a belief and each belief shapes the next thought. Treating a decision as a single moment hides that. Treating it as a loop makes the mechanism visible, and a pattern that repeats can be observed and interrupted. This is descriptive working language for the loop, not a claim of external scientific validation.
Can an organisation measure whether its decision-making quality is declining?
It can observe leading indicators, which is a weaker and more honest claim than measuring quality directly. Observable signals include whether dissent still reaches decision-makers, whether decision rationale is recorded at the time rather than reconstructed later, whether the range of options considered is widening or narrowing, and whether structured reflection still happens on a cadence. None of these prove decision quality. They indicate whether the conditions that support good judgment are intact, which is what can actually be watched before an outcome forces the question.
A note on precision
Framework names on this site carry ™ and never ®, and no page describes this work as a new science, as scientifically proven, or as clinically validated. That restraint is deliberate. The Research and Claims Statement sets out the evidence standard in full.