Large Language Models (LLMs) are increasingly used in high-stakes domains like law and research, yet their inconsistencies and response instability raise concerns about trustworthiness.This study evaluates six leading LLMs-GPT-3.5, GPT-4, Claude, Gemini, Mistral, and LLaMA 2-on rationality, stability, and ethical fairness through reasoning tests, legal challenges, and bias-sensitive scenarios.Results reveal significant inconsistencies, highlighting trade-offs between model scale, architecture, and logical coherence.These findings underscore the risks of deploying LLMs in legal and policy settings, emphasizing the need for AI systems that prioritize transparency, fairness, and ethical robustness.

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.