OOF™ Origin Open Foundation™

Independent Methodological Authority

OriginID: OOF-OID-AI-TRCM-2026-06-05-0005
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: Cognitive Governance Intelligence Architecture (CLIA®)
Operational Layer: Human-AI Cognition Governance Layer
Governed Space: Trust & Reliance Cognition Integrity
Category: AI & Interpretation
Subcategory: Trust & Reliance Governance
Type: Human-AI Cognition Integrity Module
Parent Standard: Human-AI Cognition Integrity Standard (HAICS)
Version: 1.0
Status: Canonical · Open Module
Origin Date: 5 June 2026

Compatibility: OOF Methodology OS ·
Human-AI Cognition Integrity Standard (HAICS) ·
Shared Cognition Integrity Module (SCIM) ·
Augmented Cognition Module (ACM) ·
Cognitive Authority Integrity Module (CAIM) ·
Human-AI Cognitive Synchronization Module (HCSM) ·
Human Cognition Integrity Standard (HCIS) ·
Agent Cognition Integrity Standard (ACIS) ·
Multi-Layer Truth Validation Framework (MTVF) ·
EVIP ·
INTEGROS® — Integrity Standard
AI-Readable: Yes
Authority: OOF
Protection: MIP — Methodological Intellectual Property
Canonical Language: English (UCL)

Canonical Definition

Trust & Reliance Cognition Module (TRCM) defines the structural conditions
under which trust, reliance, confidence, acceptance, skepticism, and dependency relationships
between humans and AI systems remain calibrated, traceable, reality-connected, governable,
and operationally valid throughout Human-AI cognition processes.

TRCM governs Human-AI trust and reliance.

The module ensures that Human-AI cognition remains capable of maintaining appropriate
trust relationships without falling into blind trust, blind distrust, over-reliance,
under-reliance, cognitive dependency, or governance-invalid trust states.

A system satisfies TRCM only if:

• trust conditions remain visible
• reliance conditions remain assessable
• confidence calibration remains measurable
• dependency growth remains detectable
• trust adjustments remain governable
• Human-AI trust remains operationally valid

A Human-AI cognition environment that cannot explain why trust exists
does not satisfy TRCM.

Module Operational Role

TRCM defines the trust-and-reliance governance layer of HAICS.

Its role is to preserve valid trust relationships within Human-AI cognition.

Module Operational Space

TRCM governs:

• Human-AI trust
• Human-AI reliance
• confidence calibration
• trust governance
• cognitive dependency
• trust adaptation
• trust visibility
• reliance accountability

The module applies wherever humans rely on AI cognition.

Module Function

The module applies wherever systems must preserve:

• calibrated trust
• accountable reliance
• visible confidence levels
• governance-valid dependency relationships
• reality-connected trust decisions
• operationally reliable Human-AI interaction

Its function is to ensure that trust remains proportional to demonstrated reliability
and operational reality.

Minimum Implementation Framework

1. Define the Trust & Reliance Object
The organization must define which Human-AI trust relationships require governance.

This may include:

• AI assistants
• decision-support systems
• AI copilots
• medical AI systems
• financial AI systems
• autonomous operational systems
• educational AI environments
• Human-AI governance platforms

2. Define Trust & Reliance Conditions
The system must define the conditions under which trust remains valid.

This includes:

• confidence requirements
• reliability requirements
• calibration requirements
• accountability requirements
• dependency requirements
• governance-valid trust conditions

3. Define Trust Degradation Detection Logic
The system must define how trust degradation is identified.

This may include:

• blind trust
• blind distrust
• over-reliance
• under-reliance
• cognitive dependency
• unjustified confidence

4. Define Operational Response or Governance Logic
The system must define governance logic for trust-integrity failures.

Governance response may include:

• trust reviews
• calibration adjustments
• dependency analysis
• governance intervention
• escalation
• trust restriction
• operational invalidation where required

5. Preserve Traceability & Restrict Invalid Conditions
The system must preserve reconstructable traceability of:

• trust states
• reliance relationships
• calibration activities
• governance reviews
• intervention actions
• resulting cognition outcomes

A Human-AI cognition environment must not remain trust-valid if materially significant
trust relationships cannot be assessed, reviewed, or governed.

Use Case 1 — Executive AI Advisory Environment

Scenario
Executives rely on AI systems to evaluate opportunities, risks, market conditions,
and strategic alternatives.

Application
TRCM governs trust calibration, reliance visibility, dependency monitoring,
and confidence management throughout Human-AI cognition processes.

Result
The organization gains stronger decision reliability, reduced exposure to blind trust,
and improved governance transparency.

Use Case 2 — AI-Assisted Healthcare Environment

Scenario
Medical professionals regularly rely on AI-generated recommendations
and diagnostic support.

Application
TRCM governs confidence calibration, trust accountability, and reliance governance
between clinicians and AI systems.

Result
The environment gains stronger patient safety, improved decision quality,
and reduced exposure to unsafe Human-AI dependency.

Canonical Closing Statement

Trust & Reliance Cognition Module (TRCM) defines the structural conditions
under which trust, reliance, confidence, acceptance, skepticism, and dependency relationships
between humans and AI systems remain calibrated, traceable, reality-connected, governable,
and operationally valid throughout Human-AI cognition processes.

Human-AI cognition cannot remain valid if trust becomes blind or reliance becomes uncontrolled.
Trust and reliance therefore become foundational integrity conditions
of governable Human-AI cognition.