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Ethics of AI in Life Sciences: Universality, Cultural Diversity and Architecture of Care

Overview

This framework addresses ethical concerns in artificial intelligence applications to life sciences and health research by grounding ethics in the biological reality of how the human brain is built and socialized. The paper argues that ethical concerns are real but not special to AI, and should be governed by values derived from human neurobiology rather than AI-specific considerations.

Core Framework

Human Brain Computational Architecture

The human brain operates with a fundamentally different and much less costly computational architecture than current AI systems, achieved through:

1. Global Neuronal Workspace: Orchestrates information processing across distributed brain regions
2. Reward as Cycle: Reward is best described not as a quantity to maximize, but as a continuous cycle of:
   • Wanting
   • Liking
   • Satiety

Ethical Tension Framework

This creates a deep tension in ethics between:

• Universality of Ethical Judgment: Shared brain networks for global workspace and emotion
• Diversity of Morals: Content shaped by epigenetic appropriation of physical, social, and cultural particulars

Governance Paradigm Shift

If machines were built on these biological principles rather than current "unaffordable reward maximizers," governance would shift:

• From: Restraint and control
• To: Upbringing and care

Key Contributions

1. Biological Foundation for Ethics

• Grounds ethical frameworks in actual human neurobiology
• Identifies universally shared brain networks (global workspace, emotion)
• Explains individual uniqueness through epigenetic cultural appropriation

2. Alternative AI Architecture

• Proposes building machines on biological principles of wanting-liking-satiety cycles
• Suggests less costly computational alternatives to reward maximization
• Emphasizes sustainable, biologically-inspired AI design

3. Institutional Framework

• Outlines institutions required for an upbringing-based governance model
• Addresses open questions in implementing care-based AI governance
• Provides practical guidance for ethical AI development in life sciences

Applications

Life Sciences Research

• Ethical guidelines for AI-assisted health research
• Framework for responsible innovation in biomedical AI
• Principles for patient-centered AI applications

AI Development

• Alternative reward system design for sustainable AI
• Global workspace-inspired architectures for explainable AI
• Cultural sensitivity in AI training and deployment

Policy and Governance

• Regulatory frameworks based on biological reality
• Institutional designs for AI upbringing rather than restraint
• International standards balancing universal ethics with cultural diversity

Implementation Guidelines

When to Apply This Framework

• AI Ethics Review: For life sciences and health research applications
• AI Architecture Design: When developing sustainable, biologically-inspired systems
• Policy Development: For creating governance frameworks based on care rather than control
• Cross-Cultural AI: When addressing universality vs. diversity tensions
• Neuroscience-Inspired AI: For grounding AI in actual brain mechanisms

Key Principles

1. Biological Grounding: Base ethical decisions on actual human neurobiology
2. Cycle Over Maximization: Replace reward maximization with wanting-liking-satiety cycles
3. Universal Networks, Diverse Content: Recognize shared brain architecture with culturally-shaped content
4. Upbringing Over Restraint: Focus on positive development rather than negative constraints
5. Care Architecture: Design institutions around care and responsibility

Pitfalls and Considerations

Implementation Challenges

• Technical Complexity: Biological reward cycles are harder to implement than simple maximization
• Cultural Sensitivity: Balancing universal principles with local cultural contexts
• Institutional Innovation: Creating new governance structures requires significant change
• Measurement Difficulties: Quantifying care-based outcomes vs. traditional metrics

Ethical Considerations

• Avoid Anthropomorphism: Don't assume machines can truly experience wanting/liking/satiety
• Maintain Human Oversight: Ensure biological inspiration doesn't replace human judgment
• Address Power Imbalances: Care-based governance must not reinforce existing inequalities
• Consider Non-Human Impacts: Extend ethical considerations beyond human-centric frameworks

Research Gaps

• Empirical Validation: Need more evidence on effectiveness of care-based governance
• Technical Feasibility: Practical implementation of biological reward cycles in AI systems
• Cross-Cultural Studies: Understanding how universal principles manifest across cultures
• Long-term Outcomes: Measuring success of upbringing vs. restraint approaches

Related Work

• Global Neuronal Workspace Theory: Baars, Dehaene, Changeux foundational work
• Affective Neuroscience: Panksepp's work on basic emotional systems
• Computational Psychiatry: Mathematical models of reward and decision-making
• AI Ethics Frameworks: Existing approaches to responsible AI development
• Neuroeconomics: Integration of neuroscience and economic decision theory

References

• arXiv: 2608.05436
• Authors: Jean-Pierre Changeux, Gustavo Deco, Morten L. Kringelbach
• Submitted: August 5, 2026
• Subjects: Neurons and Cognition (q-bio.NC), Artificial Intelligence (cs.AI)

Activation Keywords

AI ethics life sciences, global neuronal workspace ethics, reward cycle AI, wanting liking satiety, biological AI architecture, care-based governance, universality diversity ethics, epigenetic cultural appropriation, neuroscience-inspired AI ethics