生物科学AI研究者提出了'可追溯信任'框架，帮你理清AI输出如何安全指导实验室行动。
AI已成为生物科学工作基础设施，可预测生物分子结构、设计蛋白质和优化实验条件。研究人员提出'可追溯信任'框架，用于评估AI输出指导实验室行动的决策过程。该框架要求明确支持输出的证据、所声称的能力、授权行动的阈值以及覆盖机制。三个案例研究展示了如何记录AI开始塑造科学工作时建立的信任。
Traceable Trust for action-ready artificial intelligence in bioscience
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.