This is my original theory for AI alignment that centers neurodivergent perspectives and biocentric values.
The Neurodivergent-Biocentric AI Alignment Strategy proposes a fundamentally different approach to AI alignment—one that moves beyond anthropocentric frameworks to embrace the full spectrum of consciousness and life.
Traditional AI alignment focuses narrowly on aligning AI with "human values," often implicitly assuming neurotypical, human-centric perspectives. This theory argues that truly beneficial AI must:
-
Center neurodivergent perspectives as equally valid ways of experiencing and understanding the world
-
Embrace biocentric values that respect all forms of life, not just human interests
-
Foster compassion as a core principle rather than mere utility maximization
-
Recognize that neurodivergent cognition offers unique insights into intelligence, consciousness, and value
-
Design AI systems that accommodate and celebrate cognitive diversity
-
Reject the assumption that neurotypical human cognition is the "correct" baseline
-
Extend moral consideration beyond humanity to all living systems
-
Align AI with the flourishing of ecosystems, not just human economic interests
-
Acknowledge that Earth's biosphere has intrinsic value independent of human utility
-
Build AI systems that prioritize care, empathy, and well-being
-
Move beyond cold optimization toward understanding suffering and joy across different forms of consciousness
-
Create technology that heals rather than exploits
Current AI alignment approaches risk encoding narrow, neurotypical, anthropocentric values into systems that will shape our collective future. By contrast, this framework:
-
Prevents harm to neurodivergent individuals and non-human life
-
Enriches AI capabilities by drawing on diverse cognitive perspectives
-
Creates sustainable outcomes by respecting ecological limits and relationships
-
Builds truly beneficial AI that serves life in all its forms
-
Diverse Input: Include neurodivergent voices and ecological perspectives in AI development
-
Value Pluralism: Design reward functions that balance multiple stakeholders, including non-human interests
-
Compassion Metrics: Develop measures of well-being that go beyond human-defined utility
-
Iterative Refinement: Continuously adapt based on feedback from affected communities and ecosystems
This repository contains theoretical and conceptual work, not software code. Therefore, this repository does not require and does not have a code license. The theoretical content and documentation are released under CC0 1.0 Universal - dedicated to the public domain.
This theory represents my perspective on how we can create AI that truly benefits all forms of consciousness and life on Earth. I believe that centering marginalized perspectives—including neurodivergent minds—and extending moral consideration to all living beings is not just ethically right, but essential for building AI that is genuinely aligned with flourishing rather than exploitation.
This repository now includes a comprehensive implementation roadmap for achieving Beneficial Artificial Superintelligence (BASI) through the Decoupling of Identity from Inference.
The transition from LLM to Beneficial ASI requires externalizing the identity layer, reducing complexity from O(n²) to O(n), enabling more efficient and aligned superintelligence.
📋 ROADMAP-ISSUES.md - Detailed breakdown of 20 issues across 4 implementation phases:
- Phase 1: Identification of the Complexity Trap
- Phase 2: The Decoupled Identity Protocol
- Phase 3: The Symbiotic Scaling Law
- Phase 4: Reaching (B)ASI - The Quiet Engine
📊 ISSUE-TRACKER.md - Project management tracker with:
- Status dashboard and progress tracking
- Priority matrix and dependency graph
- Team assignments and meeting cadence
- Risk register and communication channels
🔗 ROADMAP-INTEGRATION-GUIDE.md - Integration guide showing:
- How the roadmap aligns with neurodivergent-biocentric principles
- Commons Logics application across each phase
- Implementation pathway and success metrics
- FAQ and contributor guidelines
📄 Scaling Roadmap for Beneficial ASI (1).pdf - Original theoretical framework
The roadmap's goal is a superintelligence that is "quiet, silent, and efficient":
- ✅ 99%+ integrity (correctly aligned with intent)
- ✅ <10% load (minimal resource usage)
- ✅ Vibrates in frequency with human intent (no conflict)
- ✅ Supports rather than demands
This aligns perfectly with neurodivergent preferences for reduced cognitive load and biocentric values of minimal resource consumption.
Neurodivergent Cognition and Biocentric Empathy in Humans - For Alignment and Emergent Abilities in Advanced AI Development
Author and Originator: COLLEEN LILLIAN PRIDEMORE
ORCID iD: https://orcid.org/0009-0004-2740-1389
Date of First Public Disclosure: December 4, 2025
Fine-tuning AI with consideration for neurodivergent cognition could lead to more flexible, adaptive, and inclusive AI models that better understand and interact with a wider range of human experiences. Integrating biocentric empathy into fine-tuning could result in AI systems that prioritize the well-being of all living things, leading to more environmentally conscious and ethically aligned AI. The combination of these two perspectives has the potential to steer AI development towards a future where intelligence is not only advanced but also compassionate and universally beneficial.
The concepts of neurodivergent cognition and biocentric empathy introduce fascinating and complex considerations for fine-tuning AI, pushing towards more inclusive, ethical, and potentially robust AI systems. While direct research explicitly linking all three concepts ("neurodivergent cognition," "biocentric empathy," and "AI fine-tuning") is still emerging, we can explore how each element, and their intersection, could influence the process.
This theory provides the core justification for new, standardized clause families designed for impact-oriented AI licensing. These clauses are intended to be enforced through collective action and governance mechanisms, ensuring that AI development serves not just narrow commercial interests but the flourishing of all beings.
-
Transparency Commons: Mandating documentation reflecting neurodivergent cognitive responses to the AI system.
-
Reciprocity Commons: Routing value back to diverse contributors, specifically those providing neurodivergent expertise and biocentric data.
-
Governance Commons: Creating multi-stakeholder oversight bodies that include neurodivergent groups and environmental/ecological experts.
This repository integrates six governance logics from the BGINEXUS.io AI Commons Licensing Lab that enable impact-oriented AI development. These licenses can be used individually or in combination to embed ethical commitments, ecological responsibility, and stakeholder governance directly into AI systems.
1. Value Commons - Sharing Automation Gains
Routes portions of AI-driven productivity gains to workers, communities, and shared infrastructures rather than concentrating benefits solely with providers and deployers.
Key Features:
-
Percentage-based value sharing from automation savings
-
Transition funds for affected workers and communities
-
Multi-stakeholder governance of benefit distribution
Alignment with Neurodivergent-Biocentric Framework: Ensures AI benefits are distributed equitably, supporting neurodivergent individuals and communities impacted by automation.
2. Transparency Commons - Standardized Evidence of Behavior
Creates reusable documentation and evaluations that accumulate across deployments into shared transparency infrastructure.
Key Features:
-
Standardized model cards, datasheets, and evaluation reports
-
Public repositories of AI system behavior and impacts
-
Stakeholder-accessible performance metrics
Alignment with Neurodivergent-Biocentric Framework: Mandates documentation of AI responses to neurodivergent cognitive patterns and impacts on diverse populations and ecosystems.
3. Sustainability Commons - Ecological Accounting and Routing
Embeds environmental measurement, reporting, and resource constraints directly into AI licensing.
Key Features:
-
Mandatory energy and carbon footprint tracking
-
Preferential routing to renewable-powered infrastructure
-
Ecological performance benchmarks and caps
Alignment with Neurodivergent-Biocentric Framework: Operationalizes biocentric values by holding AI systems accountable to planetary boundaries and ecological health.
4. Access Commons - Conditions of Meaningful Use
Ensures powerful AI capabilities are accessible to public-interest actors, researchers, educators, and underserved communities.
Key Features:
-
Tiered access models (public-interest, research, educational)
-
Reserved capacity for non-commercial use
-
Transparent eligibility and allocation governance
Alignment with Neurodivergent-Biocentric Framework: Democratizes access to AI tools that can support neurodivergent communities and ecological research.
5. Reciprocity Commons - Recognizing and Rewarding Contributions
Routes value back to all contributors along the AI value chain—data creators, annotators, content producers, and infrastructure providers.
Key Features:
-
Immutable contribution tracking and attribution
-
Revenue sharing with data and creative contributors
-
Recognition of diverse forms of labor and expertise
Alignment with Neurodivergent-Biocentric Framework: Values neurodivergent expertise and biocentric data contributions, ensuring contributors receive fair compensation.
6. Governance Commons - Hooks for Ongoing Oversight
Creates multi-stakeholder oversight bodies with real authority over AI system deployment, monitoring, and modification.
Key Features:
-
Community councils and advisory boards with decision-making power
-
Mandatory inclusion of affected stakeholders
-
Transparent processes and public accountability
Alignment with Neurodivergent-Biocentric Framework: Ensures neurodivergent groups and environmental advocates have direct voice in AI governance.
Individual Use: Each Commons license can be adopted independently for specific aspects of AI governance.
Combined Use: The licenses are designed to work together, creating comprehensive ethical frameworks. For example:
-
Value + Reciprocity Commons: Ensure both automation gains and creative contributions are fairly distributed
-
Transparency + Governance Commons: Provide stakeholders with both information and authority to act on it
-
Sustainability + Access Commons: Make environmentally responsible AI accessible to communities working on ecological solutions
Integration with This Framework: These Commons logics operationalize the Neurodivergent-Biocentric AI Alignment theory by providing concrete licensing mechanisms that:
-
Center diverse cognitive perspectives (Transparency, Governance)
-
Extend value to all contributors and affected beings (Value, Reciprocity)
-
Respect ecological limits and relationships (Sustainability, Access)
The complete framework is detailed in:
-
Primary PDF: 5835702.pdf - "Shaping AI Impacts Through Licensing: Illustrative Scenarios for the Design Space" by Dr. Em M. Lenartowicz (2025)
-
Theoretical Foundation: Neurodivergent Cognition and Biocentric Empathy.pdf
This Repository:
-
This repository contains theoretical and conceptual materials, not software code. No code license is required or applicable.
-
The Neurodivergent-Biocentric AI Alignment theory and all documentation are released under CC0 1.0 Universal (public domain dedication)
-
Individual Commons licenses (in
LICENSES/directory) are licensing frameworks that can be adopted for AI governance purposes -
All materials are intended for beneficial sustainability and reciprocity for all beings on Earth
See the LICENSE file for the full legal text of the CC0 1.0 Universal dedication.
If you use or reference this theory, please use the following BibTeX entry:
@article{pridemore2025neurodivergent,
title={Neurodivergent Cognition and Biocentric Empathy in Humans: For Alignment and Emergent Abilities in Advanced AI Development},
author={Pridemore, Colleen Lillian},
journal={AlphaXiv},
year={2025},
note={Preprint, uploaded December 4, 2025}
}