Journal of Research and Development
Open Access
ISSN: 2311-3278
ISSN: 2311-3278
The integration of web3 community governance, using Weighted Directed Acyclic Graphs (WDAGs) and validation pools with reputation staking in combination with a federated communications protocol, offers a transformative approach to AI model optimization. This framework supports decentralized, dynamic, evolutionary and participatory AI governance, crucial for handling the complex ethical and operational demands of various AI technologies. Specifically, deep learning models gain from decentralized data handling that mitigates bias and enhances privacy through community-validated updates. Federated learning benefits from enhanced security and privacy through block chain’s transparency and immutability, with smart contracts automating model validation and updates. Transformer AI models benefit from continuous adaptation to new data facilitated by real-time updates, ensuring relevance and compliance with evolving linguistic and cultural norms. Graph Neural Networks (GNNs) utilize decentralized data to improve relational data processing, crucial for tasks like social network analysis and fraud detection. Reinforcement Learning (RL) models thrive in the varied scenarios presented by a decentralized framework, enhancing decision-making in complex environments. Lastly, Reinforcement Learning from Human Feedback (RLHF) models benefit from the broad and transparent integration of human feedback, aligning AI behavior with real-time and evolving human values and ethical standards. Collectively, these mechanisms ensure AI models are not only technically proficient but also ethically aligned and socially responsible, fostering trust and broad acceptance in AI applications. The proposed AI governance model paves the way for AI systems that are adaptable, ethical and efficiently managed, meeting the rapid evolution of technology and societal expectations.
Published Date: 2025-04-18; Received Date: 2024-07-11