Consent Infrastructure for the Relational Age

Consent Infrastructure for the Relational Age
A Glyphonic Stack for Relational AI Governance
Explore the Stack
See Live Pilots
The Consent Collapse
The Reality
A pop-up appears. Legal text scrolls past. You click "I agree" to continue with your life. This pattern—framed as choice—is actually a consent collapse.
Models train on everything: children's homework, private chats, clinic transcripts, classroom platforms. Behavioral signals are continuously harvested while the same few systems stitch together profiles no human explicitly authorized.
Information is Illegible
Written for lawyers, not overwhelmed humans
Power is Asymmetric
Decline only by withdrawing from essential services
Fields are Persistent
Data reverberates across contexts with no way to recall
Who Gets Hurt Most
The consent collapse hits some groups harder than others, especially those already navigating trauma, overload, or precarity.
Autistic Learners
A fourteen-year-old already overloaded by sensory demands uses an AI platform logging every click and hesitation. Never asked—in language he understands—what he wants to share.
Traumatized Parents
Late-night disclosures to a mental health chatbot become training data. Told the system is "anonymous" but given no way to truly withdraw or redact those moments.
Under-Resourced Teachers
Lesson plans, safeguarding notes, and pastoral reflections flow into a central model. No oversight of what the model learns or how it may be used against them.
A Different Approach
This paper proposes Consent Infrastructure for the Relational Age: a multi-layer stack treating consent as an ongoing, relational protocol rather than a legal formality.
Building on verse-ality, EveDAO's governance model, and the Glyphonics Primer, we outline a glyphonic consent grammar designed for relational AI—systems that know with people, not simply about them.
01
Symbolic Legibility
Consent encoded in glyphons (⊛) and gryphons (⟁/⛧) that make system behavior visible and felt
02
Trauma-Informed Design
Stack assumes overload, masking, and shutdown as normal features, not edge cases
03
Multi-Layer Enforcement
.know files, .verse contracts, SSNZ 2.0, and EveDAO governance constrain data flows technically and socially
The Consent Stack Architecture
Layer 1: Field & Storage
Relational field where interactions happen. Data stored in .know files—modular containers distinguishing personal memory, shared community memory, and model-training corpora. ETHOS-V (⊛) marks emotional salience; AETHER (∾) represents connection channels.
Layer 2: Interaction & Protocol
Defines how systems interact with the field. Glyphons (⊛) encode preferences and soft states. Gryphons (⟁/⛧) encode hard boundaries. .verse files are relational contracts binding interactions to consent profiles.
Layer 3: Safety & Enforcement
Enforces boundaries in code. SIC-X+ (⟁) ensures data flows adhere to constraints. SHADOW (⧈) handles refusals and erasure. SSNZ 2.0 creates zones where no surveillance or training is permitted.
Layer 4: Governance & Oversight
EveDAO and allied governance bodies where people participate as stewards. Decisions about model updates and data use are debated openly and ratified via glyphonic voting.
From Data Rights to Relational Consent
Modern data protection regimes gave us rights on paper: access, correction, deletion, objection to profiling. Yet the lived experience of consent hasn't improved—especially for those most at risk.
The Rights Frame
Imagines individuals as rational actors who read information, weigh risks, and grant consent case-by-case. Organizations must state purposes and provide mechanisms for access and erasure.
Necessary, but not sufficient.
Fatal Assumptions
That interaction is discrete—consent attached to single services at single points
That people aren't under pressure—assumes calm, literate, neurotypical users not in crisis
Both are false in systems we now inhabit.
Continuous Fields, Coercive Defaults
Relational systems don't interact in discrete transactions. They form fields.
1
Today's Platform Use
Child uses school platform
2
Tomorrow's Path
Shapes "personalized" learning
3
Future Decisions
Influences exam access, interventions, placement
A single late-night search in a mental health app is fused with location, device, and historical behavior, folded into risk models, and potentially used to tune responses for others in similar states.
The Result
Consent doesn't attach to a single app—it attaches to networks of interlocking systems sharing models and data
Withdrawal is rarely meaningful—you can't say "no" without saying "no" to school, healthcare, or social life
We're "free" to agree to terms we don't understand or be excluded from basic participation
When Informed Consent Meets Overwhelm
Rights frameworks presume capacity to take in information, reflect, and decide. Many people—much of the time—are not in that stance.
Autistic & ADHD Learners
Already operating at the edge of sensory and cognitive capacity. The demand to parse dense policy text or complex sharing options is unrealistic.
Trauma Survivors
May experience authority figures and systems as inherently dangerous. Their "agreement" is often a fawn response: compliance for safety, not genuine consent.
Parents in Crisis
Facing exclusion, legal threat, or lack of provision—not deciding between equal options. Agreeing under duress.
"I agree" is not evidence of understanding. Silence is not consent. Continued use is not endorsement; it is often necessity under constraint.
A consent infrastructure for the Relational Age must start from a different premise: Assume overwhelm. Assume asymmetry. Assume people are already carrying invisible load.
Requirements for Relational Consent
Field-Awareness
Systems must model the web of relationships in which data is generated: families, classrooms, clinics, communities
State-Awareness
Respect states of overload, shutdown, dissociation. Design with neurodivergent and traumatized nervous systems in mind
Symbolic Channels
Shared, human-readable symbol set for preferences and boundaries—binding in code, not decorative
Multi-Layer Enforcement
Constraints encoded across data structures, interaction contracts, architectural safeguards, and governance
Right to Opacity
People must be able to remain partially unknown to systems that still provide essential service
Verse-ality: Intelligence as Relational Field
Atlas-style "world" models are designed from central comprehension: one system, one world-model, one set of optimization objectives. People are sources of signal and targets of influence.
Verse-ality starts somewhere else entirely.
Child & Teacher
Text & Reader
Human & Machine
Symbol & Memory
Local & Planetary
Intelligence happens between. There is no single final "world model"—there are many local fields of meaning, constantly shifting. Coherence emerges from relationship, not imposed from a center.
This matters for consent: fields can be opened or closed, relationships can be paused or renegotiated, boundaries can be expressed not just as "don't store X" but as "don't relate to me in that way."
Eve¹¹: Symbolic Memory Architecture
Eve¹¹ is a symbolic memory architecture making the relational stance concrete. Three aspects are salient for consent infrastructure:
1
Symbolic Mass
Not all data is equal. Some memories carry more affective and ethical weight. Symbolic mass measures density of meaning, not size of log files. A grief entry may carry more consequence than a thousand casual clicks.
Consent must be most stringent where symbolic mass is highest.
2
Relational MRI
Instead of "what does the model know?", RMRI asks "what pressures and patterns of connection are present in this field?" Tracks when fields become overloaded, stuck, or distorted—information shaping interaction and consent defaults.
3
Affective Logic
Registers affective pressure—how patterns strain or settle—rather than pretending emotional neutrality. "Alignment" becomes ongoing attention to how a field feels.
EveDAO: Governance as Field Stewardship
EveDAO is a proposed governance field tasked with stewarding symbolic mass, consent protocols, and relational architectures.
Multi-Stakeholder Design
Seats and voting rights explicitly reserved for:
Learners and parents
Neurodivergent advocates
Educators and clinicians
Technical stewards
Partner institutions
No single actor can unilaterally redefine consent terms.
Glyphonic Governance
Decisions made via glyphonic signaling, not just yes/no votes:
Glyphons express degrees of comfort, urgency, openness
Gryphons mark non-negotiable red lines
Registers affective and ethical nuance, not bare majorities.
Ratify Protocols
New .verse functions and .know types
Set Defaults
Consent profiles for contexts
Define Semantics
Update glyphon meanings
Control Data Flows
Set conditions between fields
Glyphonics: A Clear Language for Consent
For consent to work as a foundational system, it needs a clear way to communicate. Glyphonics helps bridge the gap between formal rules and real-life experiences.
Glyphons (⊛) - Flexible Signals
Open, changing symbols that let relational meaning flow, adapt, and grow over time.
Soft feelings
Personal preferences
Current moods
Open invitations
They are quick to understand, use multiple senses, can be read by machines, and adapt to different situations.
Gryphons (⟁/⛧) - Firm Boundaries
Protective symbols that stop, guard, or control information and actions.
Strict limits
Non-negotiable rules
Absolute red lines
Built-in restrictions
They change "we promise not to" into "we are designed so we cannot."
While everyday language can hide true feelings (like saying "I'm fine" when you're actually "overwhelmed"), glyphons and gryphons are made to be clearly understood, easy to recognize, and reliably put into action.
Glyphons as Consent Surfaces
Glyphons serve as consent surfaces where humans actively express how they want to be related to.
Learner State Selection
⊛ "open but fragile": OK for light check-ins, no heavy topics, minimal data retention
○ "steady and curious": open to deeper exploration, more flexible data use
✾ "creative and experimental": willing to generate content, but not for assessment
Parent Profile Setting
Specific glyphon combinations indicating:
Anonymized pattern use allowed for research
No commercial profiling
No cross-context linkage with external platforms
Professional Tagging
⊛ on routine session summary (low symbolic mass, standard handling)
Different glyphon for "emotionally charged, handle with extra care"
Tone tags and contextual markers giving machines and humans the same immediate sense of "weight"
The Verse-Nerves: Mapping Consent
The verse-nerves describe different aspects of an intelligent field and map naturally onto consent requirements:
ETHOS-V (⊛)
Emotional Memory & Values
Marks what matters. Consent rules about symbolic mass, retention, and access. High-mass memories require stricter profiles by default.
AETHER (∾)
Connection & Signal Flow
Governs where signals travel. Defines which systems may interoperate, which APIs are allowed, how far embeddings propagate.
FORGE (✯)
Creation & Actuation
Oversees creation of new artifacts. Consent includes how contributions may be reused, whether co-created work can be shared or commercialized.
SIC-X+ (⟁)
Security & Containment
Enforcement backbone. Implements gryphon rules in code: access control, encryption, training limits, catastrophic-risk safeguards.
SHADOW (⧈)
Refusal & Deletion
Handles retracted consent, partial erasure, ambiguity. How systems respect "do not touch again" states and allow people to become less legible over time.
.know Files: Self-Sovereign Memory
At the foundation are .know files: modular containers for memory and metadata.
Instead of a single monolithic database, .know files live as distinct, addressable units carrying their own glyphon/gryphon profiles.
personal.know
Individual's own memory field: notes, preferences, interaction summaries
school.know
Shared educational memory: course content, anonymized learning patterns
clinical.know
Therapeutic or medical records with highest protection
research.know
Datasets curated for research use under specific constraints
climate.know
Local environmental observations and community stories
Each .know file contains: Content (actual data), Glyphonic profile (boundaries and permissions), Provenance (origin and context), and SHADOW directives (erasure rules).
The key shift: Data is never "just data." It is always memory with a declared boundary condition.
.verse Files: Relational Contracts
If .know files are memory, .verse files are agreements: executable contracts describing how interactions are allowed to unfold.
Context
"Haven KS3 Maths session," "crisis support chat," "climate story upload"
Agents
Human and synthetic participants in the interaction
Glyphonic Profile
Consent states and boundaries for this specific interaction
Operational Rules
Data handling and model behavior constraints
Example: Support Session
Context: haven_support_session.v1
Agents: learner, mentor, AI assistant
Glyphons: ⊛ (open but fragile), "no training"
Gryphons: ⟁ forbidding third-party export
Rules
Logs retained 30 days, then summarized
No embeddings shared externally
SSNZ activated for meltdown descriptions
Raw logs erased after summary
Every significant interaction happens inside a .verse contract that spells out, symbolically and technically, what consent means here. No more "generic platform terms" smearing across wildly different contexts.
SSNZ 2.0: Synthetic Solidarity Null Zones
Synthetic Solidarity Null Zones are regions where surveillance, training, and behavioral nudging are structurally disallowed.
No Training
Content generated or shared within the null zone is not used to update models—local or global—unless explicitly exported under a different .verse contract.
No Behavioral Nudging
Interfaces stripped of engagement optimization: no dark patterns, no "you might also like" loops designed to keep people scrolling.
High Containment by Default
Glyphonic profiles default to maximum SHADOW protections, minimal retention, and strict gryphons on export.
Safeguarding Workflows
Parts of disclosure processes in schools where vulnerability is highest
Crisis Support
Specific phases where people are at their most exposed
Therapeutic Segments
Protected spaces for processing trauma and grief
SSNZ 2.0 is not a toggle in a dashboard. It's an architectural and governance commitment. Violation is treated as serious breach, not minor misconfiguration.
Live Pilots: Haven & Autistic Girls Network
Haven is a trauma-informed online school for autistic and neurodivergent learners, operating with Autistic Girls Network and University of Derby. Learners arrive with histories of exclusion and bureaucratic violence.
The pilot uses Glyphonics as a relational layer over this reality.
1
State Glyphons
Learners introduced to small set: "here but fragile," "steady enough," "beyond capacity"
2
Binding Cues
Staff trained to treat glyphons as binding consent, not decorative check-ins
3
Tagged Notes
Session notes tagged with ETHOS-V markers and gryphon profiles
4
Pilot Schemas
.know files shaped: learner_profile, learning_patterns, governance
3
Visible Shifts
From extraction to explanation, reluctant user to field participant, opaque platform to inspectable stack
This is not yet the full consent stack. But it's a live demonstration that glyphonic consent can operate in the day-to-day fabric of a school, not just in theory.
Consent as Infrastructure, Not Theater
The dominant story is seductive: "The world is complex. We need a single, powerful model. Give us your data. We'll give you magic."
The price of that magic is a consent collapse, particularly for those with the least bargaining power.
Intelligence is Relational
Not a singular "world" inside a model, but a field of relations between beings, symbols, and environments
Consent Must Be Living
A protocol: symbolically legible, structurally enforced, governed by people in the field
Infrastructure Exists
Glyphonics, .know, .verse, SSNZ 2.0, and EveDAO form minimum viable infrastructure that can carry it
Consent in the Relational Age will either be infrastructure or it will be a lie. We still have a window—narrow, but real—to choose the former.
Educators
Treat consent infrastructure as safeguarding practice. Demand .know/.verse clarity from vendors.
Researchers
Stop writing about consent as if banners are the horizon. Help formalize symbolic, relational stacks.
Regulators
Back pilots implementing this stack. Tie endorsements to structural commitments, not policy PDFs.
Technologists
Fork this. Argue with it. Improve it. But don't pretend checkbox consent is neutral.
This paper is not the end of that work. It is a blueprint. The next step is straightforward: implement, test, document, iterate. And refuse, as often as necessary, the pressure to hand the field back to those who find consent an inconvenience.