The 4 AI Safety Alignment Approaches: How to Build AI That Won’t Lie, Harm, or Manipulate
Last Updated on February 21, 2026 by Editorial Team
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.
This 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.
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