Filtered Data Enhances AI Safety and Reliability
Originally Published 5 months ago — by Fortune
Researchers found that filtering risky content from AI training data, such as bioweapons instructions, can create safer models without harming performance, highlighting the importance of pre-training safeguards over post-training tweaks. The study emphasizes transparency and proactive safety measures in AI development, contrasting with industry secrecy.