Mechanisms
9
Distinct epistemic mechanisms
ArgMed debate, hard-to-vary scoring, falsification criteria, IDK protocol, safety routing, clinician feedback loop, composable domains, rules-as-data, accuracy ascertainment.
Hard to Vary
The first health AI built on good explanations.3
Most healthcare AI systems optimize for prediction accuracy. We optimize for explanation quality. The difference: predictions can be right for wrong reasons and silently fail when conditions change. Explanations that are hard to vary — where every component is load-bearing — remain correctable and auditable even when wrong.1
The standard is Karl Popper's. The criterion is David Deutsch's refinement. The architecture below operationalizes both. See the mechanisms →
Figure 1.1
Good explanations — easy to vary vs hard to vary
“The quest for good explanations is, I believe, the basic regulating principle not only of science, but of the Enlightenment generally.”
David Deutsch — The Beginning of Infinity
Engineering facts
What the architecture commits to.
Verdicts
4
Discrete supervision outputs
APPROVE, ROUTE, HARD_STOP, REQUEST_MORE_INFO. Bounded outputs make every verdict testable; ambiguity is engineered out by construction.
HTV score
0.0–1.0
Hard-to-vary range
Four-dimensional algorithm — interdependence, specificity, non-ad-hocness, falsifiability — scored per claim. Low-HTV explanations route to a clinician.2
The cycle
Conjecture, refutation, error correction.
Popper's contribution to the philosophy of science was the insight that a theory's value lies not in how often it is confirmed but in how readily it could be falsified. We apply this standard to clinical AI as a five-stage operational loop.
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01
Observe
Clinical observation enters as structured evidence — typed, hashed, provenance-tagged. No free-text intermediation between source and reasoning.
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02
Conjecture
The reasoning agent (Deutsch) proposes specific, falsifiable explanations — not probability distributions over possibilities. Each conjecture must specify what would prove it wrong.
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03
Critique
Multiple agents attack the conjecture adversarially. The hard-to-vary score is computed across interdependence, specificity, non-ad-hocness, and falsifiability.
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04
Survive
Surviving conjectures pass to the supervision agent (Popper), structurally independent of the reasoner. Same input, same verdict — deterministic.
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05
Verdict
APPROVE, ROUTE, HARD_STOP, REQUEST_MORE_INFO. Each verdict carries its evidence hash, its falsification criteria, and the override path back into step 02.
Mechanisms
Nine mechanisms compose the architecture.
01 · ArgMed Debate
Multi-agent generator → verifier → reasoner pipeline. Generate hypotheses, attack each adversarially, keep only the survivors.
02 · HTV Scoring
Quantify how hard to vary each explanation is on a 0.0–1.0 scale across interdependence, specificity, non-ad-hocness, and falsifiability.
03 · IDK Protocol
Twelve specific uncertainty triggers with structured responses. The system admits what it does not know, in a form a clinician can act on.
04 · Falsification Criteria
Every claim specifies what would prove it wrong. Claims without falsification criteria are rejected at the conjecture stage.
05 · Safety Routing
High-risk decisions and low-HTV verdicts route to a human clinician. The default is approval; the surface area for automation is bounded by epistemic confidence.
06 · Clinician Feedback Loop
Overrides actively change future reasoning for that patient. Override tracking carries confidence decay over time.
07 · Composable Domains
Medication, nutrition, exercise, sleep, mental health — the same epistemic principles compose across all clinical domains.
08 · Rules as Data
Interaction rules are explicit, versioned, auditable data. The deterministic policy engine enforces safety boundaries; rule packs declare them.
09 · Accuracy Ascertainment
We measure our own predictions against outcomes. Reproducibility is the precondition for the loop in step 06 to compound.
The thesis
Persephone explains nothing. Earth's 23.5-degree axial tilt explains everything — and the difference is the architecture.
Both stories account for the seasons. The first one is easy to vary: substitute any gods, any emotions, any relations — the explanation still "works." The second one is hard to vary: change the tilt angle and the predictions break, change the orbital plane and the hemispheres flip, change the seasons and the geometry no longer accounts for them.
Most healthcare AI today is Persephone. It produces answers that sound plausible because the underlying causal story can be rewritten without affecting the recommendation. We chose the harder constraint: every architectural commitment in this section is what it takes to ship Earth's-tilt-grade explanations.
Figure 3.1 — Hard-to-vary scoring, four dimensions
Figure 4.1
Conjecture-refutation, operationalized
Why this matters
Three audiences, the same epistemic commitment.
For patients
Your clinician stays in control — medication changes are always reviewed and approved. Explanations come with reasons. The system tells you when it does not know. Decisions are grounded in your specific situation, not generic advice.
For clinicians
The AI proposes start / stop / titrate / hold; you decide. Audit trails carry epistemological metadata for every decision. Low confidence triggers routing to you. Your overrides actively change future recommendations.
For AI safety
Error correction over error prevention. Fallibilism over certainty. Explanation over prediction. The architecture refuses to accept a verdict it cannot justify and refuses to silence a correction it cannot integrate.1
Read more
Adjacent sections.
Epistemology
How we know what we know — the Popperian foundation, the four supervision verdicts, and the structural independence between reasoning and supervision.
Read the approach →Trust
Security architecture, deterministic evaluation, HIPAA posture, and open standards — the engineering decisions that make every claim above auditable.
See the trust architecture →About
An infrastructure company, not a SaaS dashboard. Self-funded, two-org structure, clinical systems deployed at medical centers in Central Asia.
Read about Regain →See the conjecture-refutation loop in your domain.
We will trace any clinical claim from the source observation to the surviving conjecture to the supervision verdict, and answer the epistemological question against the codebase, not the brochure.
Request a demoFootnotes
- Deutsch, D. The Beginning of Infinity: Explanations That Transform the World. Allen Lane, 2011. The hard-to-vary criterion is introduced in Chapter 1, "The Reach of Explanations"; the seasons example is on pp. 1–3. Chapter 7, "Artificial Creativity," is the AI-safety reading we draw on directly.
- Popper, K. R. The Logic of Scientific Discovery. Hutchinson, 1959. The falsifiability criterion is developed in §I and §IV; the asymmetry between confirmation and refutation is the load-bearing argument of the book and is what the supervision agent in step 04 enforces.
- "Good explanation" used here in David Deutsch's sense — hard to vary while still accounting for the phenomenon. Operationalized in our supervision protocol as the 4-dimensional rubric scored 0.0–1.0 (interdependence, specificity, non-ad-hocness, falsifiability). To our knowledge, no other deployed clinical AI system encodes this criterion as runtime supervision; if you find one, please write to anton@regain.ai.