HATM — Human and AI Trust Compatibility Module

Parent Standard: Trust Layer Standard
Category: Governance & Enforcement
Subcategory: Human and AI Trust Compatibility
Type: Trust Architecture Module
Version: 1.0
Status: Canonical · Open Module
Effective Date: 10 May 2026
Compatibility: OOF Methodology OS · Trust Layer Standard · Truth Validation Layer · Integrity Standard · Universal Canonical Language · Origin-Bound Identity · Audit in Real Time · AI Runtime Integrity Vault
Authority: OOF
Protection: MIP — Methodological Intellectual Property
Canonical Language: English

Canonical Definition

Human and AI Trust Compatibility Module defines the structural
conditions under which trust may remain compatible, understandable,
and continuously verifiable across both human interpretation and
AI-executed operational logic.

A system satisfies HATM only if:

trust conditions remain readable for humans and machine-operable for AI systems
trust does not depend on human-only assumptions or machine-only logic
meaning remains stable across human and AI interpretation
operational trust can be verified without contradiction between human judgment and machine execution
trust compatibility remains structurally preserved across hybrid environments

A system that appears trustworthy to humans but cannot be
operationally validated by AI, or appears valid to AI while
remaining unintelligible or misleading to humans, does not satisfy HATM.

Module Function

HATM defines the compatibility layer of trust architecture between
human and machine-governed environments.

It ensures that trust is not split into two disconnected realities:

one for human interpretation
one for machine execution

The module applies wherever trust must remain stable across:

human oversight
AI-supported systems
autonomous execution
hybrid operational environments
cross-system interpretation
machine-readable governance
human-facing validation conditions

Its function is to preserve one structurally compatible trust-state
across both human and AI-operated environments.

Step 1 — Define the Trust Compatibility Object

The organization must define what trust relationship must remain
compatible across humans and AI systems.

Minimum requirement:

the trust object is explicit
the compatibility scope is bounded
undefined compatibility targets are excluded from valid trust logic

The trust object may include a:

system
process
workflow
AI agent
operational decision path
hybrid human-AI environment
machine-assisted governance structure

Step 2 — Define Human Trust Conditions

The system must define what makes the trust-state understandable and
valid from the human side.

Minimum requirement:

human trust conditions are explicit
the trust-state is interpretable by accountable human actors
trust does not depend on opaque machine output alone

These conditions may include:

understandable meaning
accountable decision logic
reviewable validation status
visible responsibility structure
non-deceptive interpretation boundaries

Step 3 — Define AI Trust Conditions

The system must define what makes the trust-state machine-readable
and operationally valid from the AI side.

Minimum requirement:

AI trust conditions are explicit
trust logic is interpretable under machine-operable structural rules
trust does not depend on vague human intuition alone

These conditions may include:

canonical meaning consistency
machine-readable validation logic
runtime traceability
structured integrity conditions
bounded execution rules

Step 4 — Define Compatibility Logic

The system must define how human trust conditions and AI trust
conditions remain aligned.

Minimum requirement:

compatibility logic is explicit
contradiction between human-readable trust and machine-executed trust can be identified
trust compatibility is not assumed merely because both humans and AI participate

This means the system must ensure that:

what humans believe they are trusting
and what AI is actually executing under trust conditions
remain structurally compatible.

Step 5 — Define Meaning Stability Across Human and AI Interpretation

The system must define how trust-related meaning remains stable
across both human and machine interpretation.

Minimum requirement:

trust-related terms remain canonically consistent
semantic drift between human reading and AI execution is restricted
trust compatibility does not collapse through divergent interpretation of the same condition

Without stable meaning, compatibility becomes symbolic rather
than operational.

Step 6 — Preserve Compatibility Traceability

The system must preserve traceability of human and AI trust alignment.

Minimum requirement:

compatibility evidence is reviewable
trust alignment remains auditable
later reconstruction of trust compatibility remains possible

If compatibility cannot be reconstructed, then trust across humans
and AI becomes another unverified claim.

Step 7 — Restrict Invalid Human and AI Trust Design

The system must not be treated as valid if trust appears aligned
only at the surface while structural incompatibility remains underneath.

Minimum requirement:

invalid trust compatibility conditions are identifiable
symbolic alignment is excluded
trust cannot be claimed as compatible if humans and AI operate under different effective trust logic

Scenario

A human supervisory team relies on an AI-supported system for
operational decisions, but the machine interprets trust conditions
differently from the human reviewers.

Application

HATM is used to ensure:

trust conditions are explicit for both human and AI interpretation
canonical meaning remains stable across both sides
compatibility gaps between human oversight and machine execution can be identified
trust does not appear aligned only because both sides use the same words differently