Big model audits can be compatible with the decentralized architecture of the Web through a combination of modular verification, blockchain-based transparency, and federated evaluation mechanisms. Here’s how it works and an example:
Decentralized systems (e.g., Web3 or peer-to-peer networks) lack a central authority, so audits must focus on verifying individual components (e.g., model weights, training data, or inference outputs) rather than the entire system. Auditors can decompose the model into smaller, verifiable units—such as checking for bias in specific layers or validating data provenance—and ensure compliance with decentralized standards.
Example: A decentralized AI marketplace allows developers to upload model checkpoints. Auditors use zero-knowledge proofs to verify that a model adheres to fairness criteria (e.g., no discriminatory outputs) without accessing the full proprietary dataset.
Blockchain can record immutable audit trails, such as model updates, training datasets, or evaluation results. Smart contracts can enforce compliance rules (e.g., requiring periodic bias checks) and allow stakeholders to verify audits without trusting a central party.
Example: A decentralized autonomous organization (DAO) governs an open-source LLM. Every model update is logged on a blockchain, and auditors submit reports as transactions. Users can query the blockchain to confirm the model’s latest audit status.
In decentralized networks, audits can be performed collaboratively by multiple independent parties. Each auditor evaluates a subset of the model’s behavior (e.g., toxicity detection or factual accuracy) and aggregates results without centralized coordination.
Example: A Web3-based AI tool relies on a network of node operators to test the model’s responses for harmful content. Each node reports findings, and a consensus mechanism determines the final audit outcome.
For enterprises building decentralized AI systems, Tencent Cloud Block Storage (CBS) can securely store audit logs, while Tencent Cloud Blockchain enables transparent record-keeping. Tencent Cloud TI-ONE supports federated learning and modular model validation. These services help align large model audits with decentralized principles.
By combining these approaches, big model audits can thrive in decentralized environments without compromising trust or scalability.