Defining AI Safety Paradigms: Constitutional AI and RLHF

Originally published at adiyogiarts.com Examine AI safety in 2026, comparing Constitutional AI and Reinforcement Learning from Human Feedback (RLHF). Discover critical tradeoffs for ethical, AI development and future alignment. HOW IT WORKS Defining AI Safety Paradigms: Constitutional AI and RLHF Understanding the emergent field of AI safety requires a clear distinction between its leading paradigms. Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique designed to optimize large language models (LLMs), like ChatGPT and Claude, to better align with human preferences and values. This approach integrates direct human feedback into the reward function of a reinforcement learning process, refining model behavior based on human judgment. Fig. 1 — Defining AI Safety Paradigms: Constitutional AI an Conversely, Constitutional AI (CAI) aims for AI alignment through a comprehensive set of explicit, human-articulated principles, effectively a “constitution.” CAI system