Decentralized Data Validation for Ethical AI Training

• Published in: 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) pp. 1 - 6
• Main Authors: Sheeba, R., Mahto, Jay Prakash, Ansari, Syed Sabith, Surani, Zian Rajeshkumar, Chinnasamy, P., Alagarsamy, Manjunathan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2025
• Subjects: Artificial intelligence; Blockchain; Blockchains; Byzantine Fault Tolerance; Decentralized AI; Ethical AI training; Ethics; Fault tolerance; Fault tolerant systems; Federated learning; GDPR Compliance; Merkle-Patricia Trie; Non-Markovian Rewards; Training; Transformers; Zero knowledge proof; Zero-Knowledge Proofs
• DOI: 10.1109/ICCRTEE64519.2025.11053001

The model presented in this work represents a paradigm shift that sets a completely novel standard for data distributed validation in ethical AI training. Our new paradigm integrates fault-tolerant Byzantine consensus along with zero-knowledge proofs for secured and provable auditing of data within decentralized AI systems. The framework uses a two-layer blockchain design that separates metadata anchoring from validation logs, allowing it to achieve an instantaneous compliance check time of less than one second while maintaining privacy compliance to GDPR. Key innovations comprise a sharded Merkle-Patricia Trie kind for dynamic data lineage chains, the application of differential privacy and federated learning with bias-neutralizing validation oracles, as well as the design of incentive engineering under a non-Markovian reward system for multiple agents. The results of experimentation prove that, under adversarial conditions, the detection of anomalies is 40% faster than centralized alternatives, while maintaining an integrity verification of the audit trail at 99.99%. The collaboration between AI explainability matrices and post-quantum secure voting mechanisms in this work set innovative standards for decentralized ethical oversight of mission-critical operations, thus transforming the trust dynamics among model developers, data subjects, and auditors.