Traceable Trust for action-ready artificial intelligence in bioscience

Huayu Xin; Yizhi Cai; Mukilan Deivarajan Suresh; Gavin Michael Farrell; Iwona Gajda; Charlie Harrison; Conor Houghton; Mato Lagator

cs.CYAug 18, 2026

Traceable Trust for action-ready artificial intelligence in bioscience

Authors: Huayu Xin, Yizhi Cai, Mukilan Deivarajan Suresh, Gavin Michael Farrell, Iwona Gajda, Charlie Harrison, Conor Houghton, Mato Lagator,

Organizations: Science, Technology and Innovation Studies, School of Social and Political Science, University of Edinburgh, Edinburgh, UK. · Manchester Institute of Biotechnology, University of Manchester, Manchester, UK. · School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK. · School of Computing, Newcastle University, Newcastle upon Tyne, UK. · Protecting Crops and Environment, Rothamsted Research, Harpenden, UK. · Department of Biomedical Sciences, University of Padova, Padova, Italy. · European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. · Bristol Robotics Laboratory, University of the West of England, Bristol, UK. · Department of Computer Science, Aberystwyth University, Aberystwyth, UK. · Informatics Research Institute, University of Louisiana at Lafayette, Lafayette, LA, USA. · School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK. · Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. · Department of Computer Science, School of Science, Loughborough University, Loughborough, UK. · School of Computer Science, University of Nottingham, Nottingham, UK. · Helmholtz AI, Artificial Intelligence Cooperation Unit of the Helmholtz Association, Germany. · Institute of Computational Biology, Computational Health Center, Helmholtz Center, Munich, Germany. · School of Computation, Information and Technology, Technical University of Munich, Germany. · German Center for Diabetes Research, Munich, Germany.

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.

Trust in AI

Trust in artificial intelligence concerns when people and institutions should rely on AI, and how systems can make that reliance warranted and safe.

See all 174 papers

A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
Jul 17, 2026Andrea FerrarioAI Assurance AI Governance
Institute of Biomedical Ethics and History of Medicine, University of Zürich, Zürich, Switzerland · SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland · ETH Zürich, Zürich, Switzerland
Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governance
Sep 14, 2026Candace S. Y. Chan, Aris Karatzikos, Ilias Georgakopoulos-SoaresAI Safety AI Governance
Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Dell Paediatric Research Institute, Austin, TX, USA.
Measuring Biological Capabilities and Risks of AI Agents
Jun 18, 2026Patricia Paskov, Jeffrey Lee, Kyle Brady +1AI for Science AI Agent Evaluation
RAND Corporation, Santa Monica, Calif.

Jul 17, 2026J/K move · Enter open · S save

A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance

Andrea Ferrario

Institute of Biomedical Ethics and History of Medicine, University of Zürich, Zürich, Switzerland · SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland · ETH Zürich, Zürich, Switzerland

AI Assurance AI Governance Trust in AI

Open paper AskPDF arXiv

AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Existing approaches remain either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect multidimensional trustworthiness evidence with governance decisions. We propose a lightweight methodology centered on trustworthiness level functions: auditable rules that map measured trustworthiness profiles to governance-relevant levels. The methodology separates the underlying trustworthiness evidence from the governance rule used to interpret it and treats that rule as a lifecycle governance object. The rule may remain expert-defined or, when available evidence warrants empirical learning, be approximated by an interpretable candidate model. An AI lifecycle governance procedure embeds this choice in explicit decision gates for determining whether learning should be attempted and whether a learned candidate should become operative. The resulting rule supports lifecycle monitoring through level transitions, boundary margins, and profile drift, with explicit human responsibilities for validation, approval, and reassessment. We illustrate the methodology on synthetic AI lifecycle scenarios involving degradation, shocks, updates, heterogeneous monitoring cadences, and system comparison. Our methodology does not replace expert or legal judgment, but makes the governance interpretation of trustworthiness evidence more explicit, auditable, and contestable over time.

Abstract

Trust in AI

Explore similar work

A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance

Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governance

Measuring Biological Capabilities and Risks of AI Agents