Traceable Trust for action-ready artificial intelligence in bioscience
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 clai
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 57%Reproducibility in Computational Biology: Best Practices for AI and ML Workflows - Technology Networks →
- PossiblePossibly related (embedding) · 56%Researcher poisons open-weight AI model for under $100 →
- PossiblePossibly related (embedding) · 53%How AI helps scientists design the next generation of medicines →
- PossiblePossibly related (embedding) · 50%From virtual experiments to biomedical insight with synthetic data →
- FuzzySimilar title/name (fuzzy) · 84%liguodongiot/llm-action →
“Fuzzy title match (0.92): “Traceable Trust for action-ready artificial intelligence in ” ≈ “liguodongiot/llm-action””
- FuzzySimilar title/name (fuzzy) · 66%owainlewis/awesome-artificial-intelligence →
“Fuzzy title match (0.78): “Traceable Trust for action-ready artificial intelligence in ” ≈ “owainlewis/awesome-artificial-intelligence””
- LinkedLinked via arxiv author · 85%Huayu Xin →
“Traceable Trust for action-ready artificial intelligence in bioscience”
- LinkedLinked via arxiv author · 85%Yizhi Cai →
“Traceable Trust for action-ready artificial intelligence in bioscience”