Introduction Purpose, formalization, evaluation, and binding within AI Foundations
AI Foundations seeks to bind independently recoverable truths about human–AI systems into a compact formal framework of defined constructs, relations, and testable propositions.
Definitions follow the relation.
AI Foundations does not begin by defining something and then treating the definition as proof that the underlying relation exists.
Formalize second. Definition is not evidence by itself.
A construct is useful when it gives a precise name and boundary to a relation that can be recovered from the underlying system, history, or evidence independently of the terminology used by AI Foundations.
Evaluate before binding.
AI Foundations evaluates candidate definitions to determine whether they faithfully formalize independently recoverable relations in human–AI systems.
Definitions are tested for fidelity, boundary accuracy, determinacy, uncertainty preservation, and reproducibility before being bound into the framework.
State the candidate relation and its proposed definition precisely enough to examine and challenge.
Test whether the relation can be recovered and whether the candidate definition preserves the relevant distinction.
Determine what the evidence supports, where the definition succeeds, and where it fails, overreaches, or remains underspecified.
Decide whether the candidate definition should be kept, revised, or rejected on the basis of the evaluation.
When a relation is bound, use that supported state to constrain subsequent inference, testing, and framework development.
Binding is not automatic and does not guarantee truth. It records that a candidate definition has survived the framework’s current evaluation strongly enough to enter the formal structure with its evidence, provenance, boundaries, and limitations preserved.
Four questions govern the evaluation.
Baseline and definition-conditioned evaluation are used together with boundary cases and an explicit binding decision to determine what the candidate definition is actually doing.
Is the relation independently recoverable?
The relevant evidence is evaluated without supplying the formal AI Foundations definition. This tests whether the underlying relation can be recovered independently of the framework vocabulary.
Does the candidate definition preserve and formalize it correctly?
The candidate definition is supplied and the same underlying relation is evaluated again. The comparison reveals whether formalization preserves, clarifies, improves, alters, or distorts the independently recoverable judgment.
Where does the definition fail or overreach?
Cases near or beyond the proposed boundary test whether the definition incorrectly includes, excludes, forces, or leaves unspecified relations that the evidence does not support.
Keep, revise, or reject?
The evaluation ends in an explicit disposition. A candidate definition enters the framework only when the evidence supports binding it in its current form.
What a candidate definition must preserve.
Definitions are evaluated not only for whether they produce an answer, but for whether they accurately preserve the structure, boundaries, and uncertainty of the relation they are intended to formalize.
Fidelity
Does the definition preserve the independently recoverable relation rather than replacing it with a different one?
Boundary accuracy
Does the definition include what belongs within the relation and exclude what does not?
Determinacy
Does the definition resolve cases that should be resolvable without forcing certainty where the evidence does not permit it?
Uncertainty preservation
Does the definition preserve genuine indeterminacy rather than converting missing or ambiguous evidence into a forced conclusion?
Reproducibility
Can the same formal distinction be recovered across repeated evaluations, cases, or evaluators under comparable conditions?
Distortion check
Does supplying the definition skew judgment, introduce unsupported distinctions, or suppress evidence that should remain operative?
Binding is earned by evaluation.
AI Foundations does not require a candidate definition to outperform unaided reasoning in order to be useful.
If independent evaluators already recover the same relation without the formal vocabulary, that can be evidence that the framework is formalizing a distinction that exists independently of the framework.
The question is whether the candidate definition faithfully captures that relation, preserves its boundaries and uncertainty, and remains reproducible when applied beyond the case from which it was developed.
Definitions that fail those tests may be revised or rejected rather than incorporated into the framework.
Recover. Formalize. Test. Bind. Continue.
The objective is to identify, delimit, formalize, and test relations that are independently recoverable from human–AI systems—and bind supported relations into a framework precise enough to be examined, challenged, reused, and extended.