A new framework called Traceable Trust is proposed to ensure the responsible use of artificial intelligence in bioscience research. This framework aims to create a documented and reviewable process for decisions made based on AI outputs, particularly when those outputs guide laboratory actions. Traceable Trust addresses critical questions about the evidence supporting AI predictions, the claimed capabilities, delegated agency, action authorization thresholds, override protocols, and how outcomes feed back into future decisions. The proposal is illustrated with case studies involving ecosystem resources, project design, and direct laboratory actions, demonstrating how to build documented trust in AI-driven scientific work. AI
IMPACT Establishes a framework for documenting trust in AI outputs guiding scientific actions, potentially improving AI adoption in research.
RANK_REASON The cluster describes a research paper proposing a new framework for AI in bioscience.
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- artificial intelligence
- arXiv
- CORE Recommender
- Hugging Face
- Traceable Trust
- AI models
- ecosystem resources
- laboratory action
- proteins
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