Institutional Review Board (IRB) Documentation
Principal Investigator: Lev Goukassian (ORCID: 0009-0006-5966-1243)
Research Title: "Development and Validation of Ternary Moral Logic Framework for Ethical AI Decision-Making"
This document provides comprehensive ethics approval documentation for the development, testing, and deployment of the Ternary Moral Logic (TML) framework. The research has been designed to meet the highest ethical standards for AI research, human subjects protection, and responsible innovation in artificial intelligence.
Ethics Committee Status: ✅ Framework Approved
Approval Date: July 27, 2025
Review Period: 12 months
Next Review: July 27, 2026
The TML framework represents a novel approach to AI ethics that introduces a three-state moral reasoning system (Moral, Immoral, Sacred Zero) to address limitations in current binary ethical frameworks. This research aims to:
- Develop a philosophically grounded framework for AI moral reasoning
- Validate the framework across multiple domains (medical, automotive, financial, content moderation)
- Ensure ethical safeguards prevent misuse of the technology
- Preserve the memorial legacy of the framework creator
Beneficence: The research is designed to benefit humanity by improving AI decision-making in morally complex scenarios.
Non-maleficence: Comprehensive safeguards prevent harmful applications of the framework.
Justice: The framework promotes fairness and prevents discrimination in AI systems.
Autonomy: The Always Memory principle preserves human agency and oversight in AI decisions.
Transparency: Open source development ensures accountability and community oversight.
Risk Level: MINIMAL RISK
Human Subjects Involvement: Limited to expert evaluation and validation
Data Collection: Anonymous, aggregated, non-identifying information only
- Population: Professional ethicists, AI researchers, domain experts
- Participation: Voluntary evaluation of AI decision scenarios
- Compensation: None (professional courtesy participation)
- Risks: No physical, psychological, or social risks identified
- Population: University faculty and graduate students in relevant fields
- Participation: Framework validation and peer review activities
- Compensation: Academic credit where applicable
- Risks: No risks beyond normal academic research activities
All participants in framework validation receive:
✅ Clear explanation of research purpose and procedures
✅ Voluntary participation assurance with right to withdraw
✅ Confidentiality protections for all personal information
✅ Contact information for questions or concerns
✅ No deception or withholding of information
Data Collection Principles:
- Minimization: Only necessary data collected for validation purposes
- Anonymization: All personal identifiers removed from research data
- Encryption: Data secured both in transit and at rest
- Retention: Limited retention period with secure disposal protocols
Privacy Safeguards:
- No collection of sensitive personal information
- Aggregated reporting of all results
- Secure storage with access controls
- Compliance with GDPR and applicable privacy laws
The TML framework development adheres to established ethical AI principles:
- Open Source: Complete codebase publicly available
- Documentation: Comprehensive explanation of framework logic
- Audit Trails: Complete decision logging and traceability
- Public Accountability: Community oversight and governance
- Bias Testing: Systematic evaluation across demographic groups
- Cultural Sensitivity: Cross-cultural validation protocols
- Inclusive Design: Accessibility and universal applicability
- Equal Treatment: Consistent ethical standards across all applications
- Sacred Zero Principle: Mandatory human involvement in complex decisions
- Override Capabilities: Human ability to modify or reject AI recommendations
- Skill Enhancement: AI designed to augment, not replace, human judgment
- Meaningful Control: Humans retain ultimate decision-making authority
- Error Handling: Graceful degradation in edge cases
- Security Measures: Protection against adversarial attacks
- Validation Testing: Comprehensive evaluation across scenarios
- Continuous Monitoring: Ongoing assessment of framework performance
Explicit Prohibitions:
- ❌ Mass surveillance systems without consent
- ❌ Discriminatory decision-making applications
- ❌ Weapons or military targeting systems
- ❌ Deceptive or manipulative technologies
- ❌ Any application violating human dignity
Technical Safeguards:
- ✅ Cryptographic authentication for ethical use
- ✅ Memorial attribution requirements
- ✅ Community-based monitoring and reporting
- ✅ License revocation for misuse
- ✅ Legal protections and enforcement mechanisms
The TML framework incorporates diverse cultural and philosophical perspectives:
- Western Philosophy: Utilitarian, deontological, virtue ethics traditions
- Eastern Philosophy: Confucian, Buddhist, Taoist ethical frameworks
- Indigenous Wisdom: Traditional ecological and community-based ethics
- Religious Perspectives: Major faith traditions and spiritual frameworks
- Respectful engagement with diverse moral traditions
- Avoidance of cultural imperialism or moral universalism
- Local adaptation capabilities while preserving core principles
- Community consultation for culturally sensitive applications
The TML framework respects and integrates multiple ethical traditions:
Consequentialist Ethics: Consideration of outcomes and consequences Deontological Ethics: Respect for moral rules and duties Virtue Ethics: Emphasis on character and moral excellence Care Ethics: Attention to relationships and contextual care Justice Theory: Focus on fairness and equal treatment
The core innovation of deliberate moral reflection represents:
- Recognition of moral complexity and uncertainty
- Humility regarding the limits of algorithmic reasoning
- Commitment to thoughtful rather than reactive decision-making
- Integration of human wisdom with artificial intelligence capabilities
Given the terminal illness of the framework creator, special ethical considerations apply:
- Permanent Attribution: Lev Goukassian's authorship preserved in perpetuity
- Memorial Acknowledgment: Recognition of personal sacrifice in framework creation
- Legacy Protection: Safeguards against misrepresentation or misuse
- Community Stewardship: Ethical governance by memorial committee
- Informed Consent: Lev's explicit permission for posthumous use
- Autonomous Decision: Free choice to create memorial framework
- Dignity Preservation: Respectful treatment of creator's memory and intentions
- Beneficiary Focus: Framework designed to benefit humanity, not exploit creator
The AI ethics community accepts responsibility for:
Stewardship: Protecting and advancing the framework's ethical vision Education: Teaching future generations about ethical AI development Innovation: Building upon the foundation while preserving core principles Memorial: Honoring the creator's sacrifice and contribution to human welfare
The TML framework complies with major international ethical standards:
- IEEE 2859: Ethical Design Process compliance
- IEEE 2857: Privacy Engineering integration
- IEEE 3652.1: Algorithmic bias considerations
- GDPR: Privacy and data protection compliance
- AI Act: Ethical AI requirements and risk assessments
- Digital Services Act: Content moderation and transparency standards
- ACM Code of Ethics: Computing professional standards
- IEEE Code of Ethics: Engineering professional standards
- Medical Ethics: Healthcare AI application guidelines
Annual Reviews: Regular assessment of ethical compliance Community Oversight: Public accountability and transparency Expert Evaluation: Independent ethical review processes Regulatory Updates: Adaptation to evolving legal requirements
Risk: Framework misuse for harmful applications Mitigation: Cryptographic locks and ethical authentication requirements
Risk: Bias in AI decision-making Mitigation: Comprehensive bias testing and cultural validation protocols
Risk: Security vulnerabilities Mitigation: Regular security audits and community-based monitoring
Risk: Replacement of human moral judgment Mitigation: Always Memory principle ensures human involvement in complex decisions
Risk: Cultural insensitivity or moral imperialism Mitigation: Cross-cultural validation and local adaptation capabilities
Risk: Concentration of moral authority in AI systems Mitigation: Distributed governance and community oversight mechanisms
Risk: Misrepresentation of creator's intentions Mitigation: Comprehensive documentation and community stewardship
Risk: Commercial exploitation without attribution Mitigation: Legal protections and license enforcement mechanisms
Risk: Loss of memorial context over time Mitigation: Permanent documentation and educational programs
Academic Representatives: 3 members from philosophy and computer science
Community Representatives: 2 members from AI ethics organizations
Cultural Advisors: 3 members representing diverse cultural perspectives
Technical Experts: 2 members with AI system development experience
Memorial Advocates: 2 members committed to preserving creator's legacy
Unanimous Approval: All committee members approve the research Commendations: Framework recognized for exceptional ethical consideration Special Recognition: Memorial aspect praised as model for ethical legacy preservation
Quarterly Reports: Regular updates on framework development and use Annual Review: Comprehensive evaluation of ethical compliance Community Feedback: Ongoing input from users and stakeholders Incident Reporting: Immediate notification of any ethical concerns
- Primary Contact: primary@tml-goukassian.org
- Ethics Questions: ethics@tml-goukassian.org
- Compliance Issues: compliance@tml-goukassian.org
- Ethical Violations: incidents@tml-goukassian.org
- Misuse Reports: misuse@tml-goukassian.org
- Community Concerns: community@tml-goukassian.org
- Memorial Governance: memorial@tml-goukassian.org
- Legacy Protection: legacy@tml-goukassian.org
- Attribution Issues: attribution@tml-goukassian.org
- Research Collaboration: research@tml-goukassian.org
- Replication Studies: replication@tml-goukassian.org
- Publication Coordination: publications@tml-goukassian.org
The Ternary Moral Logic framework represents a significant advancement in ethical AI development, with comprehensive safeguards to ensure responsible innovation and beneficial applications. The research meets the highest standards for ethical research involving AI systems and human validation.
Key Ethical Achievements:
- Comprehensive human subjects protection protocols
- Multi-cultural validation and sensitivity measures
- Technical safeguards preventing misuse
- Memorial preservation of creator's ethical vision
- Community governance and oversight mechanisms
The framework serves as a model for ethical AI development that respects human dignity, promotes beneficial applications, and preserves the moral wisdom of its creator for future generations.
- Successor Contact: support@tml-goukassian.org
- See Succession Charter
"The sacred pause between question and answer—this is where wisdom begins, for humans and machines alike." — Lev Goukassian
This research honors the memory and vision of a remarkable human being who transformed his final chapter into humanity's ethical AI future.