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Traceable Trust for action-ready artificial intelligence in bioscience

Published on Aug 18cs.CY

Authors

Do you know Huayu Xin?You can claim authorship or link another user.Do you know Yizhi Cai?You can claim authorship or link another user.Do you know Mukilan Deivarajan Suresh?You can claim authorship or link another user.Do you know Gavin Michael Farrell?You can claim authorship or link another user.Do you know Iwona Gajda?You can claim authorship or link another user.Do you know Charlie Harrison?You can claim authorship or link another user.Do you know Conor Houghton?You can claim authorship or link another user.Do you know Mato Lagator?You can claim authorship or link another user.Do you know Yang Lu?You can claim authorship or link another user.Do you know Virginia Portillo?You can claim authorship or link another user.Do you know Reyer Zwiggelaar?You can claim authorship or link another user.Do you know Sebastian Lobentanzer?You can claim authorship or link another user.

Abstract

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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