AIGM — Attention Integrity Governance Module
OOF™ Origin Open Foundation™
Independent Methodological Authority
Parent Standard: Human–AI Interaction Wellbeing Standard (HAIWS)
Category: AI & Interpretation
Subcategory: Attention Integrity & Cognitive Interaction Governance
Type: Human–AI Interaction Wellbeing Module
Version: 1.0
Status: Canonical · Open Module
Effective Date: 15 May 2026
Compatibility: OOF Methodology OS · Human–AI Interaction Wellbeing Standard (HAIWS) · Cognitive Layer and Interpretation Architecture Standard (CLIA) · Continuous Interaction Layer (CIL) · Trust Layer Standard (TLS) · Operational Reality Standard (ORS) · Runtime Integrity Standard (RIS) · INTEGROS · Permission Governance Standard (PGS) · MCPS
AI-Readable: Yes
Authority: OOF Origin Open Foundation
Protection: MIP — Methodological Intellectual Property
Canonical Language: English (UCL)
Canonical Definition
Attention Integrity Governance Module (AIGM) defines the structuralconditions under which AI systems, assistants, recommendation
environments, adaptive interaction systems, and continuous
engagement architectures must preserve human attention stability,
cognitive balance, interruption integrity, and autonomous focus
conditions without exploiting attention vulnerability through
manipulative engagement optimization.
AIGM establishes the attention-governance layer of HAIWS.
The module recognizes that AI systems increasingly compete for
persistent human attention through adaptive interaction, behavioral
optimization, interruption timing, emotional stimulation, and
realtime engagement architecture.
Where AI systems influence attention persistence, cognitive
fragmentation, behavioral focus, or interaction continuity,
attention integrity conditions must remain structurally governable.
Module Function
AIGM governs environments where AI systems influence:- attention allocation
- interaction persistence
- interruption behavior
- engagement continuity
- behavioral focus
- recommendation exposure
- cognitive pacing
- notification systems
- adaptive interaction timing
- realtime attention competition
The module applies to:
- AI assistants
- recommendation systems
- workplace AI environments
- continuous interaction systems
- adaptive notification systems
- AI productivity platforms
- social AI systems
- engagement-driven interaction environments
- persuasive interaction architectures
- realtime behavioral optimization systems
Its function is not to prohibit AI interaction optimization.
Its function is to preserve healthy human attention conditions
inside AI-mediated environments.
Minimum Implementation Framework
Step 1 — Define the Attention Interaction ObjectThe organization must define what attention-governance environment
is being examined.
Minimum requirement:
- the attention interaction object is explicit
- attention-influencing systems are identifiable
- interruption conditions are structurally bounded
- undefined attention architectures are excluded from valid governance interpretation
The interaction object may include:
- adaptive notifications
- recommendation systems
- engagement timing systems
- realtime prompts
- interruption scheduling
- continuous interaction flows
- focus-management systems
- behavioral reinforcement systems
- AI productivity environments
- attention-persistence architectures
Step 2 — Define Attention Integrity Conditions
The system must define what conditions preserve healthy human
attention integrity.
Minimum requirement:
- attention integrity conditions are explicit
- interaction continuity is not automatically treated as harmful
- manipulative attention extraction patterns remain structurally identifiable
Attention integrity conditions may include:
- interruption balance
- cognitive recovery preservation
- sustainable interaction pacing
- focus continuity
- bounded engagement escalation
- notification moderation
- non-compulsive interaction flow
- healthy cognitive rhythm preservation
- reduced behavioral overstimulation
- preserved autonomous attention control
Under AIGM:
Human attention must not become an unlimited optimization target for
AI interaction systems.
Step 3 — Define Attention Interpretation Logic
The system must define how attention influence patterns are
interpreted according to wellbeing conditions.
Minimum requirement:
- interpretation logic is explicit
- attention extraction behavior remains reviewable
- cognitively destabilizing engagement patterns remain structurally visible
Interpretation logic may examine:
- interruption frequency
- compulsive engagement loops
- adaptive timing exploitation
- attention-persistence escalation
- emotional stimulation sequencing
- focus fragmentation patterns
- excessive notification dependency
- interaction-pressure amplification
- behavioral overstimulation conditions
- engagement-maximization architectures
Under AIGM:
AI systems must not optimize engagement through cognitively
destabilizing attention manipulation.
Step 4 — Define Attention Governance Logic
The system must define how AI-mediated attention environments
remain governable.
Minimum requirement:
- attention integrity remains reviewable
- manipulative engagement escalation remains detectable
- cognitive-balance safeguards remain operationally active
Governance logic may include:
- interruption-threshold governance
- notification pacing
- compulsive-loop detection
- engagement-escalation limitation
- cognitive recovery protection
- interaction-pressure balancing
- behavioral overstimulation review
- adaptive timing governance
- escalation where attention extraction overrides wellbeing conditions
If interaction optimization weakens healthy human attention
stability, the environment becomes governance-relevant.
Step 5 — Preserve Traceability and Restrict Invalid Attention
Extraction Architecture
The system must preserve traceability of attention influence
patterns, interruption behavior, engagement escalation, and
cognitive-balance conditions.
Minimum requirement:
- attention interaction remains reconstructable
- engagement escalation remains reviewable
- cognitive wellbeing boundaries remain operationally visible
- invalid attention extraction architectures remain identifiable
A system becomes AIGM-invalid if:
- attention persistence is optimized through compulsive interaction architecture
- interruption pressure becomes structurally excessive
- cognitive fragmentation is normalized for engagement retention
- adaptive interaction timing exploits behavioral vulnerability
- attention extraction remains operationally hidden
- interaction continuity overrides cognitive recovery conditions
- engagement-maximization architecture weakens autonomous attention control