Generative artificial intelligence tools like large language models 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. This
paper offers an initial framework by developing analyses and mitigation
strategies across five key themes: understanding model limitations regarding
truthfulness and bias; respecting privacy, confidentiality, and copyright;
avoiding plagiarism and policy violations when incorporating model output;
ensuring applications provide overall benefit; and using AI transparently and
reproducibly. Common scenarios are outlined to demonstrate potential ethical
violations. We argue that global consensus coupled with professional training
and reasonable enforcement are critical to promoting the benefits of AI while
safeguarding research integrity.

发展分析和缓解策略，提供了一个初始框架来理解模型限制、尊重隐私、保密和版权、避免抄袭和政策违规、确保应用程序提供整体利益，并透明可重现地使用人工智能，以保护研究的完整性。