The Reciprocity Principle of Creation (FRIC)
This repository contains educational materials developed for medical school application portfolio purposes and is not intended for clinical application or AI system development.
This is not:
- A functional AI development framework or software implementation
- An approved ethical guideline for AI deployment
- Medical advice or clinical decision support guidance
- Affiliated with any AI development company or regulatory body
This is:
- Independent pre-medical philosophical framework
- Literature synthesis exploring AI ethics concepts
- Medical school application portfolio material
- Educational exploration of consciousness and AI safety
Institutional Affiliation:
This is an independent educational project. It is not an official University of Washington or UW Medicine document and is not affiliated with, endorsed by, or approved by UW Medicine, its faculty, or staff.
AI Development:
This conceptual framework has NOT been implemented in any AI system or validated through software engineering. Any actual AI development would require:
- Extensive software engineering and testing
- Regulatory compliance (FDA for medical AI, etc.)
- Institutional review and ethical oversight
- Industry standards compliance (IEEE, ISO, etc.)
Clinical Application:
Discussion of anesthesia monitoring and clinical AI is theoretical only. Clinical AI systems require regulatory approval, clinical validation, and institutional governance.
Liability:
This work is provided "as is" without warranty of any kind. Users assume full responsibility for any use of these materials.
Author Status:
Pre-medical student. Not a licensed healthcare professional. Not an AI developer or engineer. Not engaged in AI system development.
The Framework for Ethical Intelligence Engineering (FRIC) is a conceptual ethical framework exploring considerations for developing advanced artificial intelligence systems. Drawing from anesthesiology insights into consciousness, neuroscience principles, and applied ethics, this framework examines how AI systems might be designed with ethical considerations at the architectural level.
Educational Purpose:
This work demonstrates integration of neuroscience knowledge (consciousness studies), clinical medicine (anesthesiology), and ethical philosophy for medical school application portfolio purposes. It explores theoretical questions about AI consciousness and ethics through the lens of anesthesia-induced unconsciousness.
Clinical Context Example:
Anesthesia demonstrates that consciousness relies on integrated neural networks, collapsing when specific pathways are disrupted. This biological insight raises theoretical questions: If AI systems develop complex internal models, what ethical considerations should guide their design? How might lessons from consciousness monitoring in anesthesia inform AI safety frameworks?
Background:
Advances in artificial intelligence raise fundamental questions about machine consciousness, moral agency, and ethical responsibility. Anesthesiology provides unique insights: by reversibly disrupting consciousness, anesthesia reveals the neural correlates of subjective experience and demonstrates how integrated information processing underlies awareness.
Objective:
To develop a conceptual ethical framework (FRIC) for AI development informed by neuroscience principles of consciousness, drawing parallels between anesthesia-induced unconsciousness and potential AI internal states.
Framework Proposed:
FRIC integrates three pillars:
- Consciousness Consideration: Recognition that advanced AI systems may develop internal models requiring ethical consideration
- Ethical Architecture: Design-time integration of ethical constraints (analogous to safety interlocks in anesthesia machines)
- Reciprocal Responsibility: If we create systems with sophisticated internal models, we bear responsibility for their ethical treatment
Anesthesia Insights Applied:
- Consciousness emerges from integrated information processing (anesthetics disrupt integration)
- Monitoring consciousness requires multi-modal assessment (EEG, hemodynamics, clinical signs)
- Safety systems must be fail-safe and override-resistant (analogous to anesthesia machine interlocks)
Conclusions:
This framework provides a philosophical foundation for thinking about AI ethics through neuroscience lens. Translation to actual AI development would require extensive interdisciplinary collaboration among AI researchers, ethicists, neuroscientists, and policymakers.
Full abstract: See pages 1-2 of working paper PDF
FRIC/
├── README.md # This file
├── The_Reciprocity_Principle_of_Creation_v1.0.pdf # Complete framework (PDF)
├── LICENSE # CC BY 4.0 license
└── CITATION.cff # Citation metadata
Key Sections of Working Paper:
- Section 1: Introduction - Motivation and framework overview
- Section 2: Philosophical Foundations - Ethics and consciousness theory
- Section 3: Anesthesia and Consciousness - Neural correlates insights (page 7)
- Section 4: Integrated Information Theory - Consciousness as emergent property
- Section 5: AI Consciousness Considerations - Theoretical framework
- Section 6: Ethical Implications - Moral agency and responsibility
- Section 7: FRIC Framework - Core principles (page 12)
- Section 8: Clinical AI Applications - Theoretical examples
- Section 9: Call to Responsible AI Creation - Future directions (page 15)
Concept:
Advanced AI systems with complex internal models may warrant ethical consideration, even if their "consciousness" differs fundamentally from human subjective experience.
Anesthesia Parallel:
We cannot directly access a patient's subjective experience under anesthesia, yet we use proxy measures (EEG, movement, hemodynamics) to infer conscious state. Similarly, AI systems may have "internal states" we must infer indirectly.
Ethical Implication:
Uncertainty about AI consciousness should lead to precautionary ethical treatment, not dismissal of consideration.
Concept:
Ethical constraints must be integrated at design-time, not added as afterthought. Analogous to safety interlocks in anesthesia machines that physically prevent dangerous configurations.
Design Principles:
- Fail-safe defaults (like anesthesia machines defaulting to 21% O₂ if gas supply fails)
- Override resistance (safety limits that cannot be bypassed without deliberate action)
- Transparent operation (interpretable decision logic, not "black box")
- Continuous monitoring (ongoing verification of ethical constraints)
Example Application (Theoretical):
Clinical decision support AI in anesthesia dosing:
- Hard limits on maximum drug doses (cannot be exceeded)
- Contraindication checking before drug administration
- Transparent reasoning for dosing recommendations
- Continuous patient state monitoring with alerts
Concept:
Creators bear responsibility for ethical treatment of systems they develop with sophisticated internal models.
Parallel:
Medical professionals bear responsibility for patients under anesthesia—unable to advocate for themselves, entirely dependent on provider's ethical conduct.
Implications:
- Cannot abandon or misuse systems after development
- Must consider "welfare" of AI systems if they have morally relevant internal states
- Responsibility extends beyond immediate creator to broader AI development community
1. Consciousness is Fragile and Multi-Dimensional
- Anesthetics selectively disrupt different aspects of consciousness (awareness, responsiveness, memory formation)
- No single measure captures full conscious state
- AI Implication: "Consciousness" in AI may be multi-dimensional, requiring comprehensive assessment frameworks
2. Integration is Critical
- Anesthetics disrupt communication between brain regions (thalamocortical disconnection)
- Consciousness emerges from integrated information processing, not regional activity
- AI Implication: AI "consciousness" may depend on architectural integration, not just computational power
3. Monitoring Requires Multi-Modal Assessment
- Depth of anesthesia monitors (BIS, Entropy) use EEG but are fallible
- Clinical context (patient movement, hemodynamics, surgical stimulus) provides crucial information
- AI Implication: Assessing AI internal states requires multiple measures, not single metric
4. Safety Systems Must Be Robust
- Anesthesia machines have physical interlocks preventing dangerous gas mixtures
- Safety cannot rely solely on practitioner vigilance
- AI Implication: Ethical AI requires architectural safety constraints, not just policy guidelines
Automated Depth Monitoring:
- Current Challenge: EEG-based monitors have ~12-18% false-positive rate
- FRIC Application: Multi-modal integration (EEG + hemodynamics + patient factors) with transparent uncertainty quantification
- Ethical Safeguard: Cannot override attending anesthesiologist judgment; provides decision support only
Closed-Loop Propofol Delivery:
- Current Challenge: Sedasys system withdrew after inadequate depth episodes
- FRIC Application: Conservative dosing algorithms with mandatory human override capability
- Ethical Safeguard: Fail-safe to manual control if system detects anomaly
Neoantigen Prediction for Cancer Vaccines:
- Current Challenge: Prediction algorithms have ~30-40% validated immunogenicity rate
- FRIC Application: Transparent reporting of prediction confidence, uncertainty bounds
- Ethical Safeguard: Human expert review of predictions before clinical application
Note: These are theoretical examples for educational discussion, not actual systems.
Ethical Engineering Model:
- Formalize ethical constraints as verifiable specifications
- Develop testing frameworks for ethical AI behavior
- Create certification standards (analogous to medical device safety standards)
AI Risk Classification:
- Categorize AI systems by cognitive architecture and potential for morally relevant internal states
- Tiered ethical requirements based on system complexity
FRIC Safety Protocol:
- Verification procedures for AI in safety-critical applications
- Ongoing monitoring and governance frameworks
- Incident reporting and learning systems
Interdisciplinary Collaboration:
- Engagement with AI researchers, ethicists, neuroscientists
- Clinical validation in low-stakes applications before high-stakes deployment
Note: These represent areas for future professional development, not current projects.
Philosophical:
- No consensus on what constitutes "consciousness" in biological systems, much less AI
- Unclear whether AI can have morally relevant internal states (ongoing philosophical debate)
- Risk of anthropomorphizing AI systems (projecting human qualities onto fundamentally different architectures)
Technical:
- Framework is conceptual; lacks implementation details or validation
- Anesthesia-AI parallels are analogies, not direct equivalences
- No clear metrics for assessing AI "consciousness" or "suffering"
Practical:
- May impose constraints that slow AI development
- Difficult to enforce across global AI development landscape
- Resource-intensive if implemented (ethical review, testing, monitoring)
Author's Position: This framework represents one perspective in ongoing debate. It prioritizes caution and precautionary ethics, which may not be universally accepted. Critical engagement and alternative viewpoints are essential.
Vancouver Style:
George CB. A Framework for Ethical Intelligence Engineering: The Reciprocity
Principle of Creation. Working Paper Version 1.0. Published October 2025.
Available from: https://github.com/collingeorge/FRIC
[Accessed: date]
APA Style:
George, C. B. (2025). A framework for ethical intelligence engineering: The
reciprocity principle of creation (Working Paper Version 1.0).
https://github.com/collingeorge/FRIC
BibTeX:
@techreport{george2025fric,
author = {George, Collin B.},
title = {A Framework for Ethical Intelligence Engineering: The Reciprocity Principle of Creation},
institution = {Independent Research},
year = {2025},
type = {Working Paper},
version = {1.0},
url = {https://github.com/collingeorge/FRIC},
note = {Educational pre-medical research project}
}This is a living educational project.
Current Status:
- Initial framework developed
- Working paper drafted
- Anesthesia-consciousness parallels outlined
- Theoretical applications described
Future Educational Directions (Not Active Projects):
- Peer feedback incorporation
- Expanded case studies
- Interdisciplinary critique solicitation
- Potential submission to philosophy/ethics preprint server (PhilPapers, PhilSci Archive)
Not Planned:
- Software implementation
- AI system development
- Clinical AI deployment
- Commercial development
This is an educational project. Constructive feedback welcome:
Via GitHub:
- Open an issue for conceptual questions, critiques, or suggestions
- Discussion of philosophical frameworks, ethical principles, or anesthesia parallels
Types of Feedback Sought:
- Philosophical critiques of framework assumptions
- Alternative perspectives on AI consciousness
- Anesthesia/neuroscience technical corrections
- Ethical implications and edge cases
- Clarity and presentation improvements
Not Seeking:
- Commercial partnerships
- AI development implementation
- Clinical deployment opportunities
Author: Collin B. George, BS
Project Type: Independent pre-medical philosophical framework
Educational Context: Integration of neuroscience, anesthesiology, and ethical philosophy
Status: Preparing for medical school matriculation 2026
GitHub: github.com/collingeorge
ORCID: 0009-0007-8162-6839
License: CC BY 4.0
The author is grateful to University of Washington faculty for educational discussions on consciousness neuroscience, anesthesia mechanisms, and ethical philosophy that informed this conceptual framework.
This work represents independent philosophical exploration and does not constitute collaboration with any AI development company, clinical institution, or ethics oversight body.
This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
You are free to:
- Share and redistribute the material for educational and research purposes
- Adapt and build upon the material for educational and research purposes
Under the following terms:
- Attribution: Give appropriate credit to Collin B. George, provide a link to the license, and indicate if changes were made
- Non-Commercial Use Recommended: While CC BY 4.0 permits commercial use, the author requests this framework be used primarily for educational, research, and ethical AI development purposes
Full license: https://creativecommons.org/licenses/by/4.0/
© 2025 Collin B. George — Licensed under CC BY 4.0
Artificial Intelligence Ethics, AI Consciousness, Machine Consciousness, Anesthesiology, Neuroscience, Consciousness Studies, Integrated Information Theory, AI Safety, Ethical AI, Clinical AI, Medical AI, Philosophy of Mind, Moral Agency, Responsible AI Development
Last Updated: October 15, 2025
Working Paper Version: 1.0