Scientific laboratories can be dangerous places
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The Risks of Using AI Models in Scientific Laboratories
The incorporation of AI models in scientific laboratories has raised concerns about the potential for dangerous experiments that could lead to fires or explosions, according to researchers. While these models can provide a semblance of understanding, they may overlook essential safety measures, as demonstrated by tests on 19 advanced AI models, all of which made critical errors with potentially lethal consequences.
Although serious accidents in research labs are uncommon, tragic incidents have occurred in the past. For instance, in 1997, chemist Karen Wetterhahn lost her life due to dimethylmercury exposure, highlighting the hazards present in laboratory environments. The use of AI models in designing experiments and procedures introduces a new level of risk, particularly with large general-purpose models that may lack the necessary domain knowledge to ensure safety.
Research led by Xiangliang Zhang at the University of Notre Dame introduced the LabSafety Bench test, comprising multiple-choice questions and pictorial scenarios to evaluate an AI model’s ability to identify potential hazards and safety risks in a laboratory setting. Results from testing 19 cutting-edge AI models revealed varying levels of accuracy, with some models performing poorly in safety assessments.
While AI holds promise for enhancing scientific processes, caution is warranted when employing these models in laboratory settings. Zhang emphasized that current AI models are not adequately equipped to design experiments and stressed the importance of human oversight in ensuring safety protocols are followed.
Industry responses to these findings have been mixed, with OpenAI highlighting the capabilities of their latest science model, GPT-5.2, in supporting researchers with improved reasoning and error-detection. However, other tech giants such as Google, DeepSeek, Meta, Mistral, and Anthropic have yet to comment on the matter.
Experts like Allan Tucker from Brunel University and Craig Merlic from the University of California, Los Angeles, underscore the need for human involvement in experiment design, noting that AI models should complement rather than replace human decision-making. While AI advancements continue to evolve, the importance of instilling good safety practices in laboratory settings remains paramount.
As the debate on AI’s role in scientific laboratories continues, ongoing research and collaboration between humans and AI systems are essential to mitigate risks and ensure the safe and responsible use of technology in research environments.
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