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Control Risk for Potential Misuse of Artificial Intelligence in Science

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arxiv 2312.06632 v1 pith:WW7ZBHOJ submitted 2023-12-11 cs.AI

Control Risk for Potential Misuse of Artificial Intelligence in Science

classification cs.AI
keywords sciencerisksmisusemodelsartificialcontrolharmfulintelligence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The expanding application of Artificial Intelligence (AI) in scientific fields presents unprecedented opportunities for discovery and innovation. However, this growth is not without risks. AI models in science, if misused, can amplify risks like creation of harmful substances, or circumvention of established regulations. In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain. We first itemize the risks posed by AI in scientific contexts, then demonstrate the risks by highlighting real-world examples of misuse in chemical science. These instances underscore the need for effective risk management strategies. In response, we propose a system called SciGuard to control misuse risks for AI models in science. We also propose a red-teaming benchmark SciMT-Safety to assess the safety of different systems. Our proposed SciGuard shows the least harmful impact in the assessment without compromising performance in benign tests. Finally, we highlight the need for a multidisciplinary and collaborative effort to ensure the safe and ethical use of AI models in science. We hope that our study can spark productive discussions on using AI ethically in science among researchers, practitioners, policymakers, and the public, to maximize benefits and minimize the risks of misuse.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond

    cs.LG 2026-03 conditional novelty 6.0

    SafeSci creates a large objective benchmark and training resource that reveals safety weaknesses in current LLMs for science and demonstrates measurable improvement through targeted fine-tuning.