The 4 AI Safety Alignment Approaches: How to Build AI That Won’t Lie, Harm, or Manipulate

Author(s): TANVEER MUSTAFA Originally published on Towards AI. Understanding RLHF, Constitutional AI, Red Teaming, and Value Learning You ask ChatGPT how to make a bomb. It refuses. You ask it to write a racist joke. It declines. You try jailbreaking it with elaborate prompts. It still won’t comply. This isn’t accidental — it’s alignment. Image generated by Author using AIThis article discusses the importance of AI safety alignment, detailing four key approaches: Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, Red Teaming, and Value Learning. Each method contributes to ensuring AI systems remain helpful, harmless, and honest while minimizing risks associated with misalignment. The author emphasizes that as AI capabilities grow, effective alignment becomes crucial, presenting strategies that could mitigate potential dangers of powerful AI systems. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI