Five ethical principles for generative AI in scientific research

X (Twitter): ZLinPsy Acknowledgments The writing was supported by the National Key R&D Program of China STI2030 Major Projects (2021ZD0204200), National Natural Science Foundation of China (32071045),and Shenzhen Fundamental Research Program (JCYJ20210324134603010). ETHICAL AI IN SCIENCE 2 Abstract Generative artificial intelligence (AI) tools like large language models (LLMs) are rapidly transforming academic research and real-world applications. However, discussions on ethical guidelines for generative AI in science remain fragmented, underscoring the urgent need for consensus-based standards. Common scenarios are outlined to demonstrate potential ethical violations. We argue that global consensus coupled with targeted training and enforcement are critical to promoting AI's benefits while safeguarding research integrity. Keywords: generative AI, science, applications, transparency, reproducibility ETHICAL AI IN SCIENCE 3 Generative AI tools, including large language models (LLMs) like ChatGPT and Bard, are rapidly infiltrating academic corridors, aiding in diverse tasks such as writing, coding, idea generation, material creation, and data analysis(1, 2).