TRUST: A Framework for Decentralized AI Service v.0.1

Decentralized verification improves AI accountability: TRUST's distributed auditing approach achieves 72.4% accuracy in verifying AI reasoning—4-18% higher than centralized baselines—while eliminating single points of failure that could hide biased or faulty decisions from oversight.

Transparent reasoning traces build justified trust: By decomposing AI decision-making into five levels of abstraction and recording all decisions on-chain, the framework makes AI reasoning auditable to stakeholders, addressing the "black box" problem that currently erodes public confidence in high-stakes AI systems.

Multi-stakeholder consensus protects against manipulation: The framework's three-tier verification system (computational checkers, AI evaluators, and human experts) with stake-weighted voting ensures that malicious actors cannot game the system—maintaining integrity even when up to 30% of participants act dishonestly.

Privacy and security can coexist with transparency: TRUST uses "privacy-by-design segmentation" to prevent reconstruction of proprietary AI logic while still enabling public verification, addressing the tension between protecting companies' intellectual property and ensuring public accountability.

Root-cause analysis enables meaningful accountability: The DAAN protocol identifies which agent or component caused specific failures 70% of the time (vs. 54-63% for existing methods), enabling targeted corrections and accountability rather than opaque system-wide blame.