为生物科学中AI决策提供可追溯的信任框架
Traceable Trust for action-ready artificial intelligence in bioscience
- 提出可追溯信任框架，评估AI输出转为实验行动的可靠性
- 明确证据、权限、阈值与纠错机制，保障决策可审查
- 适合需高可信度决策的生物科研团队与实验设计者
人工智能正成为生物科学研究的工作基础设施。AI模型可预测生物分子结构、设计蛋白质、排序变异体、图像标注、菌株推荐及优化实验条件。我们主张，将AI输出用于指导实验室操作是一个关键的可信性节点，应遵循可定义、可审查的流程。为此，提出「可追溯信任」框架，用于评估输出到行动的边界：包括支持输出的证据、所宣称的能力、授权范围、行动触发阈值、可否被干预以及结果如何反馈至后续决策。通过涵盖生态资源、项目设计和实验室操作的三个案例研究，展示了如何在AI开始影响科研工作时，系统化记录信任过程。
原文摘要 · Abstract (English)
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.
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