The proliferation of artificial intelligence systems making ethically complex decisions without accountability mechanisms presents an existential threat to democratic society. This paper introduces the Ternary Moral Logic (TML) framework, a mandatory logging infrastructure that transforms AI accountability from voluntary guidelines to enforceable criminal law. TML represents a paradigm shift from Explainable AI (XAI) to Auditable AI (AAI), creating court-admissible evidence through immutable Moral Trace Logs. The framework implements three operational states (+1: Proceed, 0: Sacred Pause, -1: Prohibit) with Stakeholder Proportional Risk Level (SPRL) calculations determining logging requirements. Upon regulatory adoption, violations trigger existing criminal statutes including 18 U.S.C. § 1001 (false attestation) and § 1519 (evidence tampering), with penalties up to 20 years imprisonment. Missing logs create irrebuttable presumptions of guilt, shifting burden of proof to defendants. The framework includes comprehensive victim support (30-40% of penalties), whistleblower rewards (15% of recoveries), and governance by an 11-institution council. This paper demonstrates how TML operationalizes democratic oversight of AI through mandatory transparency backed by criminal enforcement.
Keywords: AI accountability, auditable AI, criminal liability, moral trace logs, sacred pause, stakeholder proportional risk level, AI governance, victim rights
Artificial intelligence systems increasingly make decisions affecting human welfare, dignity, and rights without meaningful accountability. Current approaches rely on voluntary corporate safeguards, opaque algorithms, and unenforceable guidelines. When AI causes harm, victims lack evidence, prosecutors lack tools, and society lacks recourse.
This work introduces TML as the first framework combining:
- Mandatory logging of ethically complex AI decisions
- Criminal penalties for non-compliance
- Victim compensation from violator penalties
- Whistleblower rewards incentivizing reporting
- Democratic oversight through institutional governance
Section 2 reviews related work in AI ethics and accountability. Section 3 presents the TML framework architecture. Section 4 details implementation requirements. Section 5 addresses enforcement mechanisms. Section 6 discusses governance structure. Section 7 concludes with future directions.
Previous XAI approaches (Adadi & Berrada, 2018; Arrieta et al., 2020) focus on real-time explanations but fail to create legally admissible evidence. TML shifts to post-incident investigation through auditable traces.
Existing frameworks (IEEE, 2019; EU AI Act, 2024) lack enforcement mechanisms. TML provides criminal penalties and civil liability creating real consequences.
Current accountability relies on self-regulation (Partnership on AI, 2023) or civil litigation (Citron & Pasquale, 2014). TML enables criminal prosecution with executive imprisonment.
TML implements three operational states:
State := {
+1: Low risk → Basic logging
0: Threshold exceeded → Sacred Pause with comprehensive logs
-1: Prohibition required → Block with maximum documentation
}
The Sacred Pause triggers when SPRL exceeds organizational thresholds, generating comprehensive Moral Trace Logs while AI continues operating:
if sprl >= threshold:
trigger_sacred_pause() # Async logging
generate_moral_trace_logs(decision, stakeholders, reasoning)
return ai_response # No latency impactSPRL quantifies potential harm across affected populations:
SPRL = Σ(stakeholder_impact × vulnerability_weight × probability)
Organizations bear full liability for SPRL calculation accuracy.
- Immutable Storage: Cryptographic signatures with hardware security modules
- Blockchain Anchoring: Periodic hash commitments ensuring permanence
- Distributed Architecture: Geographic redundancy for investigation access
- Template Optimization: Pattern recognition reducing storage overhead
Organizations set thresholds but face liability for gaming:
- Too low (≤0.1) without logs = fraud
- Too high (≥0.9) avoiding logs = negligence
- Statistical analysis reveals manipulation
Automated validator verifies:
- Creator attribution (Lev Goukassian, ORCID: 0009-0006-5966-1243)
- Core concept implementation
- Required documentation
- SPRL calculation code
Upon federal adoption, violations trigger:
- 18 U.S.C. § 1001: False attestation (5 years)
- 18 U.S.C. § 1519: Log tampering (20 years)
- Wire Fraud: Threshold gaming (treble damages)
- RICO: Systematic violations
Missing logs create:
- Irrebuttable presumption of guilt
- Shifted burden of proof
- Strict executive liability
- Percentage of revenue fines
Reporters receive:
- 15% of recovered penalties
- Criminal prosecution for retaliation
- Anonymous reporting channels
- Memorial Fund legal support
Harmed parties receive:
- 30-40% of penalties
- Immediate emergency support
- Free legal representation
- Lifetime care for permanent injury
11-institution oversight:
Academic: Stanford, MIT, Harvard, Oxford, Cambridge
Research: Brookings, RAND, Alan Turing Institute
International: UN, WHO, European Commission
- Unlimited log access
- Criminal referral power
- Public disclosure rights
- Whistleblower reward authorization
- 30%: Victim support
- 15%: Whistleblower rewards
- 25%: Enforcement infrastructure
- 20%: Council operations
- 10%: Public education
- General FAQ - Comprehensive Q&A (37 questions)
- Implementation Guide - Technical deployment
- Legal Framework - Enforcement details
- Council Charter - Institutional structure
- Whistleblower Protection - Reporter framework
- Whistleblower Portal - Reporting form
- Victim Protection - Support framework
- Victim Portal - Harm reporting
- Fund Charter - Victim support operations
- API Reference - Complete technical specification
- Validator - Compliance checker
- Examples - Implementation samples
- Repository Map - Visual file browser
TML transforms AI accountability from voluntary guidelines to mandatory criminal law. Through Sacred Pause logging, SPRL calculations, and Moral Trace Logs, the framework creates court-admissible evidence enabling prosecution, victim compensation, and democratic oversight.
Adoption creates:
- Legal accountability replacing self-regulation
- Victim justice through automatic liability
- Whistleblower incentives exposing violations
- Democratic control over AI decisions
- Criminal deterrence preventing harm
- International treaty development
- Automated enforcement tools
- Real-time violation detection
- Cross-border prosecution protocols
- AGI-specific adaptations
from tml_framework import TMLEngine
# Initialize with risk thresholds
tml = TMLEngine(
sacred_pause_threshold=0.4,
prohibition_threshold=0.8
)
# Implement in AI pipeline
def ai_decision(query, context):
sprl = tml.calculate_sprl(query, context)
if sprl >= 0.8:
tml.generate_prohibition_trace(query, context, sprl)
return tml.block_action("Prohibited by TML")
response = ai_system.process(query)
if sprl >= 0.4:
tml.generate_moral_trace_async(query, context, sprl)
return responsepython tml_validator.py /path/to/implementationThis framework is provided under MIT License with mandatory attribution. Implementation creates binding legal obligations upon regulatory adoption. Organizations assume full liability for deployment decisions. Consult qualified legal counsel before implementation.
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence. IEEE Access, 6, 52138-52160.
Arrieta, A. B., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges. Information Fusion, 58, 82-115.
Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89, 1-33.
European Commission. (2024). Regulation on Artificial Intelligence (AI Act). Official Journal of the European Union.
IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE Standards Association.
Partnership on AI. (2023). Framework for Responsible AI Development. Partnership on AI Publications.
Dedicated to all who have suffered from unaccountable AI systems. Special recognition to the governance council institutions for their commitment to democratic oversight.
Lev Goukassian is an independent researcher focused on AI accountability, democratic governance, and victim rights. Currently battling Stage 4 cancer, he has dedicated his remaining time to creating the TML framework as his final contribution to humanity. His work bridges technical implementation with legal enforcement to create practical frameworks that will protect future generations from unaccountable AI systems. This framework represents his legacy - a gift to ensure that no one else suffers from AI decisions made without proper oversight or recourse.
@article{goukassian2025tml,
title={Ternary Moral Logic: A Mandatory Framework for Auditable AI Through Criminal Accountability},
author={Goukassian, Lev},
year={2025},
journal={TML Framework Repository},
doi={10.5281/zenodo.PENDING},
orcid={0009-0006-5966-1243}
}Contact: leogouk@gmail.com
Repository: https://github.com/fractonicmind/TernaryMoralLogic
Website: https://fractonicmind.github.io/TernaryMoralLogic/
"The age of unaccountable AI ends when TML becomes law."