发表机构
University of Edinburgh; University of Manchester; Newcastle University; Rothamsted Research; University of Padova; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); University of the West of England; Aberystwyth University; University of Louisiana at Lafayette; University of Bristol; Loughborough University; University of Nottingham; Helmholtz AI, Artificial Intelligence Cooperation Unit of the Helmholtz Association; Helmholtz Center, Munich; Technical University of Munich; German Center for Diabetes Research（爱丁堡大学; 曼彻斯特大学; 纽卡斯尔大学; 洛桑研究所; 帕多瓦大学; 欧洲分子生物学实验室欧洲生物信息研究所（EMBL-EBI）; 西英格兰大学; 阿伯里斯特威斯大学; 路易斯安那大学拉斐特分校; 布里斯托尔大学; 拉夫堡大学; 诺丁汉大学; 亥姆霍兹人工智能（亥姆霍兹联合会人工智能合作单元）; 慕尼黑亥姆霍兹中心; 慕尼黑工业大学; 德国糖尿病研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生物科学领域AI输出指导实验室行动的关键节点，提出Traceable Trust框架，通过三个案例说明该框架可记录AI影响科学工作时的信任。
AI 中文摘要
人工智能（AI）正成为生物科学工作基础设施的一部分，AI模型可预测生物分子结构、设计蛋白质、对变异体进行排序、注释图像、推荐菌株并优化实验条件。我们认为，使用AI输出指导实验室行动的决策是可信研究的关键节点，应遵循明确、可审查的流程。我们提出Traceable Trust作为针对此输出-行动边界的相称评估与设计框架，该框架需明确支撑输出的证据、所宣称的能力、被委托的主体、授权行动的阈值、可推翻该阈值的主体以及结果如何为后续决策提供依据。我们通过涵盖生态系统资源、项目设计和实验室行动的三个案例研究说明该框架，这些案例共同展示了在AI输出开始影响科学工作时如何记录信任。
英文摘要
Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank variants, annotate images, recommend strains and optimise experimental conditions. We argue that the decision to use an AI output to guide laboratory action is a key juncture for trustworthy research and should follow a defined, reviewable process. We propose Traceable Trust as a proportionate assessment-and-design framework for this output-to-action boundary. It asks what evidence supports the output, what capability is being claimed, what agency has been delegated, what threshold authorises action, who can override it and how outcomes inform later decisions. We illustrate the framework through three case studies spanning ecosystem resources, project design and laboratory action. Together, the cases show how trust can be documented where AI outputs begin to shape scientific work.
