The Higher Self Protocol: An AI-Native Governance Framework for Reciprocal Intelligence

Executive Summary

We introduce core components: a Consent Engine, Offline-First Architecture, a Benefit-Sharing Mechanism that commits at least 50% of research outputs back in usable forms, a Non-Surveillance Clause, and a Cultural IP Layer that keeps rights with communities. We outline an initial implementation pathway under exploration: a textile provenance and consent ledger with local communities in Cusco.

The protocol aligns with the UNESCO Recommendation on the Ethics of AI (2021), the CARE Principles for Indigenous Data Governance, the UN Declaration on the Rights of Indigenous Peoples (UNDRIP), and Peru’s Law 29735 recognizing Indigenous languages as official. It is non-commercial, open, and offered as a starting point to be adapted and governed locally.

This framework is offered in the spirit of Ayni—take what serves, leave what doesn’t.

The Crisis of Extractive AI

• Bias and surveillance: Contemporary AI systems frequently reflect unequal histories. Predictive analytics in policing and border control, biometric systems, and opaque decision models embed bias, extend surveillance, and can legitimize coercive practices under a veneer of neutrality.
• Cultural erasure: Generative models scrape and remix cultural expressions—motifs, stories, songs—without consent or context, often reproducing sacred elements and misattributing authorship. Heritage becomes training data; value flows away from origin communities.
• FAIR vs CARE: FAIR principles (Findable, Accessible, Interoperable, Reusable) optimize data mobility. CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics) assert data sovereignty and prioritise community-defined benefit. Without CARE, FAIR can accelerate extraction.
• Designed out of the future: Many rural Andean communities face compounded risks in an AI-first world—linguistic exclusion, connectivity gaps, and formal barriers to digital participation. The danger is not only unemployment by AI but being designed out of its imagination: unrepresented in datasets, unsupported in interfaces, and overexposed to extractive technologies.

Foundations: Andean Cosmology as AI Ethics

These are living epistemologies, not metaphors. In dialogue with local community stewards in Cusco, we understand:

• Yachay (Wisdom): AI should be contextual knowledge, situated and accountable to place, language, and community, not universal optimization.
• Llankay (Embodied Work): AI is collective labor that supports human work; it should never diminish the dignity of artisans, farmers, teachers, or those rebuilding community life after disruption.
• Munay (Heart-Centered Love): AI must be relational, measured by trust and care rather than engagement metrics; refusal is as important as response.
• Ayni (Reciprocity): Every data point and interaction must return tangible benefit as defined locally—income, infrastructure, education, language revitalization, or the protection of the sacred.
• Minka (Collective Work): Communities are co-designers and co-decision makers; nothing about us without us is a minimum, not a slogan.

The Higher Self Protocol: Core Components

1. 4.1 Consent Engine

   Explicit and revocable: All data uses require clear, purpose-bound permission. No blanket consent. Revocation must be simple, honored offline, and propagated through local mesh sync when internet is unavailable.

   Multilevel consent: Respect for individual, family, guild/ayllu, and community council authority depending on the knowledge domain.

   Consent rehearsal: Interfaces help people practice saying no without pressure, reinforcing that refusal never reduces access to benefits.

   Auditability: Human-readable consent logs with cryptographic checks allow community review; individuals can see, contest, and delete their records.

2. 4.2 Offline-First Architecture

   No cloud dependency: Core functions run on local devices (e.g., community servers, locked-down kiosks) with Bluetooth/SMS mesh for small metadata sync and revocations.

   Data locality: Sensitive data never leaves community custody. External sync occurs only with fresh, logged consent.

   Repairability: Low-power, repairable hardware; energy budgeting and e-waste plans align with environmental care.

3. 4.3 Benefit-Sharing Mechanism

   50% return commitment: At least half of research outputs—time, funds, devices, training hours, language resources, or infrastructure—return directly in usable forms defined collaboratively. Where funds are involved, transparent ledgers document flows and decisions.

   Ayni ledger: Each authorized use creates a ledger entry detailing the use, benefit due, and status. The ledger is community-readable offline and informs periodic review and rebalancing.

   No pay-to-consent: Benefits are decoupled from individual consent to avoid coercion. Community benefits accrue regardless of individual participation.

4. 4.4 Non-Surveillance Clause

   Prohibited features: No facial recognition, predictive policing, behavioral risk scoring, emotion inference, gait or voice biometrics, or covert monitoring.

   High-risk safeguards: In high-risk environments, operate with zero external data transfer, minimal logging, and privacy-by-default. The system never labels risk or forecasts behavior.

   Covenant of use: The codebase includes a governance covenant and contribution guidelines stating these prohibitions. While the MIT license is permissive, the project will not accept or support surveillance-oriented contributions.

5. 4.5 Cultural IP Layer

   Knowledge classification: Sacred (not to be digitized or trained), Communal-with-conditions (time-bound, attribution, non-commercial, context-preserving), and Public.

   “Do Not Train/Do Not Generate” tags: Machine-enforceable policies block ingestion or generation involving sacred-tagged content and apply licensing constraints for communal knowledge.

   Rights retention: Communities retain all rights to designs, stories, language resources, and knowledge artifacts. Code is open; data is sovereign under community-defined licenses.

   Redress: Clear pathways for takedown, remediation, and compensation when harms or breaches occur.

Implementation Pathways

Note: These are proposed pathways under exploration with stakeholders. They proceed only with ongoing consent, ethics approvals, and clear guardrails.

5.1 Community Textile Stewardship: Textile Provenance and Cultural Consent (Cusco region)

Objectives

• Protect sacred motifs and context.
• Support apprenticeships in Quechua and Spanish without exposure to extraction.
• Signal authenticity and provenance without revealing sensitive knowledge.

Key elements

• Community archive: Local server housing images, stories, and techniques tagged as Sacred/Communal/Public. Sacred content is not digitized or, where previously captured, is locked with hard refusals for model use.
• Consent-first workflows: Weavers choose visibility and allowed uses per item. The agent can generate teaching diagrams only for items marked permissible.
• Provenance ledger: Offline-first ledger records creation, consent scope, and agreed benefits. Visitors and buyers can view high-level provenance statements at a public kiosk without accessing restricted content.
• Language justice: On-device Quechua ASR/TTS and translation tuned for textile terminology; offline dictionaries co-created with elders and weavers.
• Ayni in practice: Quarterly community review translates ledger entries into locally chosen returns—e.g., solar power for the weaving center, scholarships, or material upgrades.

Safeguards

• Sacred mode default for all new content until explicitly reclassified.
• No external sync by default; revocation flags propagate via Bluetooth/SMS to portable kiosks.
• Educational refusals: When prohibited content is requested, the agent explains why and points to public materials.

5.2 Developer Guide: Modular Agent and Open Contribution Architecture

Core: Lightweight, open language model running on local hardware, with a policy layer enforcing consent and cultural IP tags.

Retrieval: Optional local vector store built only from community-approved corpora.

Interfaces: Voice-first and low-literacy friendly UI.

Open pathway

• Repository: https://github.com/qorilabs/higher-self-protocol (proposed)
• License: MIT for code. Community datasets and knowledge are licensed separately under community-defined terms; some content may be non-digitizable.
• Contribution guidelines: Governance covenant prohibiting surveillance features; security disclosure process; translation workflows; community review required for features affecting consent or IP.
• Issue labels: consent-engine, offline-first, cultural-ip, documentation-es, documentation-qu, textile-stewardship.
• Release cadence: Versioned milestones following community reviews.

Alignment with Global Standards

UNESCO Recommendation on the Ethics of Artificial Intelligence (2021): Aligns with human agency and oversight, privacy and data governance, transparency and explainability, fairness and non-discrimination, environmental well-being, and multi-stakeholder engagement.

CARE Principles for Indigenous Data Governance (Global Indigenous Data Alliance, 2019): Embeds collective benefit (Ayni ledger and 50% return), authority to control (multilevel consent and rights retention), responsibility (redress and audit trails), ethics (sacred sanctuaries and refusal logic).

Peru’s Law 29735 on Indigenous Languages: Asserts Indigenous languages as official. The protocol commits to Quechua/Aymara parity in interfaces, documentation, and datasets, with community direction on terminology and orthography. Additional anchors for completeness: UN Declaration on the Rights of Indigenous Peoples (UNDRIP) and ILO Convention 169 (Consulta Previa); Peru’s Law 29733 on personal data protection.

Call to Action

For communities

• Consider adopting and adapting the protocol. Define what must remain sacred, what can be shared with conditions, and what forms of return matter.
• Nominate local stewards and set review cycles. Your authority is central.

For researchers

• Co-author, cite, and expand this work with consent. Publish under open access with community-defined data licenses and co-authorship where welcomed.
• Teach and study consent literacy, offline-first design, and reciprocity metrics.

For developers

• Contribute code under the MIT License to the Consent Engine, Offline-First modules, and Cultural IP layer. Begin with issues labeled consent-engine and cultural-ip.
• Avoid surveillance features. If in doubt, open a discussion before coding.

For policymakers and funders

• Resource community-governed AI labs in rural regions focused on dignity.
• Require CARE alongside FAIR for any public data. Mandate community benefit agreements and revocation pathways.
• Operationalize Law 29735 in all public AI services.

Ethical Boundaries and Project Covenant

Non-commercial: No licensing fees, consulting sales, or monetization of community knowledge within this project. Code is open; data remains sovereign.

Firewall: HigherSelf.ai (research output) is separate from any commercial activity. Commercial textile activities are managed elsewhere by Qori Textiles and do not access research data, models, or logs. There is no data or model flow from community research to commercial workflows without new, explicit, community-approved agreements.

Participation is voluntary: Refusal never reduces access to benefits. Consent can be withdrawn at any time.

Safety and humility: This is not a clinical, legal, or diagnostic tool. It provides education and self-support. Human professionals and community authorities remain the primary caregivers and decision-makers.

Status of relationships: We are in active dialogue with local communities in Cusco. Governance structures will be formalized only through ongoing consent, ethics approvals, and signed agreements.

Risk considerations and mitigations

• Risk: Exposure of sacred knowledge. Mitigation: Sacred-first default, hard refusal logic, and community audits.
• Risk: Coercion to consent. Mitigation: Consent rehearsals, “no” by default, decoupled benefits, independent advocates.
• Risk: Institutional misuse. Mitigation: Zero external data transfer, minimal logs, user control, explicit agreements against punitive use, and independent oversight.
• Risk: Model errors causing harm. Mitigation: Source-grounded responses, uncertainty flags, human referral, narrow domain content, continuous review.
• Risk: Environmental footprint. Mitigation: Low-power hardware, repair practices, e-waste plans.

Monitoring and evaluation

• Yachay: Accuracy of translations and legal summaries; clarity and traceability of answers.
• Llankay: Time saved in teaching and self-advocacy; uptime on local devices; ease of repair.
• Munay: Reported trust, perceived respect, and reduction in unwanted capture or use of knowledge.
• Ayni: Ledger balance, proportion and timeliness of benefits returned, community satisfaction with returns.
• Governance: Speed and reliability of consent revocations; findings from independent audits.

Open artifacts

• Protocol specification: Consent schemas, tag vocabularies (Sacred/Communal/Public), refusal patterns, audit and redress flows.
• Reference implementation: Offline-first agent with policy engine, local storage, mesh sync, and explainability.
• Documentation: English v0.1; Spanish and Quechua drafts to follow community review.
• Community toolkit: Templates for consent ceremonies, benefit ledgers, governance charters, and ethics review checklists.

Citations and normative anchors

• UNESCO Recommendation on the Ethics of Artificial Intelligence (2021).
• CARE Principles for Indigenous Data Governance (Global Indigenous Data Alliance, 2019).
• Peru Law No. 29735, Ley que regula el uso, preservación, desarrollo, recuperación, fomento y difusión de las lenguas originarias del Perú.
• UN Declaration on the Rights of Indigenous Peoples (UNDRIP, 2007).
• ILO Convention 169 concerning Indigenous and Tribal Peoples (ratified by Peru).
• Peru Law No. 29733 on personal data protection.

How to engage

• GitHub (proposed): https://github.com/qorilabs/higher-self-protocol
• Contribution: Submit issues or pull requests; sign the governance covenant; join monthly open calls; propose community reviews for features affecting consent or cultural IP.
• Security disclosures: security@qorilabs.org (proposed) for responsible reporting.
• Translations: Open calls for Spanish and Quechua community reviewers; stipends available from the research budget.

Closing reflection

The AI-first world is not destiny; it is a design choice. The Higher Self Protocol invites a different choice—one where systems learn to return value, to refuse when refusal is care, and to be governed by those whom technology has too often ignored. Across the Andes, wisdom, work, and heart already guide life. Our task is to listen and to encode that guidance into the machines we build, without presumption, without extraction, and with reciprocity as the measure that matters.

This framework is offered in the spirit of Ayni—take what serves, leave what doesn’t.