# Traceable Trust for action-ready artificial intelligence in bioscience

arXiv: https://arxiv.org/abs/2608.17997 · HTML: https://mlsanity.com/paper/2608.17997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [Packaging research artefacts with RO-Crate](https://mlsanity.com/paper/2108.06503/) (2021)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [Recording provenance of workflow runs with RO-Crate](https://mlsanity.com/paper/2312.07852/) (2023)
- [An AI-native experimental laboratory for autonomous biomolecular engineering](https://mlsanity.com/paper/2507.02379/) (2025)
- [Policy Cards: Machine-Readable Runtime Governance for Autonomous AI Agents](https://mlsanity.com/paper/2510.24383/) (2025)
- [Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)](https://mlsanity.com/paper/2505.16619/) (2025)
- [The Agentic Automation Canvas: a structured framework for agentic AI project design](https://mlsanity.com/paper/2602.15090/) (2026)

## Abstract (arXiv)

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.