🚨 ISSUE:
When artificial intelligence models make decisions, it is often not possible to verify the source, quality, or reliability of the training data that feeds those decisions. This poses a significant risk in terms of transparency, accountability, and trust.
---
🔍 SOLUTION: Walrus Protocol
Walrus is a decentralized data verification and resource tracking protocol. It aims to make the origin, integrity, and transaction history of the data used in artificial intelligence training processes transparent.
---
📌 KEY FEATURES (Current Information):
1️⃣ Provenance Tracking:
- Each piece of data is marked with a blockchain-based identity (hash) from its source.
- The data with which the AI model was trained, and the time of training, is recorded.
2️⃣ Quality Scoring and Verification:
- Datasets are rated with community-verifiable quality metrics.
- Suspicious or biased data can be flagged.
3️⃣ Incentive Mechanism:
- Verified data providers and validators are rewarded with $WAL tokens.
- Malicious or misleading data entries can be penalized.
4️⃣ AI Model Transparency:
- Models integrated with Walrus make the trail of the data on which their decisions are based traceable.
- This is particularly critical in sectors requiring regulation (healthcare, finance, law).
---
🌍 CURRENT STATUS:
- Walrus has completed the transition from the test network to the main network.
- Collaboration with large language model (LLM) developers and data providers.