Tag
DeepMind published the third iteration of its Frontier Safety Framework, expanding risk domains to include harmful manipulation and misalignment risks, with refined risk assessment processes and enhanced governance protocols for advanced AI models.
OpenAI researchers study worst-case frontier risks of releasing open-weight LLMs through malicious fine-tuning (MFT) in biology and cybersecurity domains, finding that open-weight models underperform frontier closed-weight models and don't substantially advance harmful capabilities.
OpenAI released an updated Preparedness Framework with sharper focus on high-risk AI capabilities, introducing clearer criteria for prioritizing risks and new Research Categories for emerging threats like autonomous replication and sandbagging alongside established Tracked Categories for biological, chemical, and cybersecurity capabilities.
DeepMind publishes a comprehensive approach to AGI safety and security, outlining a systematic framework to address misuse, misalignment, accidents, and structural risks as artificial general intelligence approaches reality within the coming years.
OpenAI conducted a study with 100 participants to evaluate whether GPT-4 meaningfully increases access to dangerous biological threat creation information compared to internet-only baselines, as part of their Preparedness Framework for AI safety. The research introduces an early warning evaluation methodology to detect AI-enabled biorisk uplift and serves as a potential tripwire for flagging models that require further safety testing.
OpenAI announced the winners of its Preparedness Challenge, which identified unique risks associated with frontier AI systems. The top ten submissions highlighted concerns including financial system manipulation, information leakage, medical harm, cyberattacks, and persuasion-based threats, with 70% of entries emphasizing AI's potential to enhance malicious persuasion capabilities.
OpenAI presents a hazard analysis framework for evaluating safety risks associated with code synthesis LLMs like Codex, examining technical, social, political, and economic impacts through a novel evaluation methodology for code generation capabilities.