Traceable Trust for action-ready artificial intelligence in bioscience
2026-08-18 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors explain that AI is increasingly used in bioscience labs for tasks like predicting molecular structures and designing proteins. They highlight the importance of carefully deciding when to trust AI recommendations to make lab decisions. To help with this, they propose a system called Traceable Trust, which guides researchers to check evidence, understand AI limitations, and keep track of decisions. They show how this approach works through three real-world examples. This helps make sure AI influences scientific work in a clear and trustworthy way.
Artificial IntelligenceBiosciencesProtein DesignDecision ThresholdTrustworthinessTraceabilityMachine LearningLaboratory AutomationScientific WorkflowCase Studies
Authors
Huayu Xin, Yizhi Cai, Mukilan Deivarajan Suresh, Gavin Michael Farrell, Iwona Gajda, Charlie Harrison, Conor Houghton, Mato Lagator, Yang Lu, Virginia Portillo, Reyer Zwiggelaar, Sebastian Lobentanzer
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.