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About this paper
Penny R. Atkins, Manish Parashar
A framework and prototype assess data trustworthiness for AI-driven science by analyzing its usage patterns, generating 'data-usage graphs' to inform fitness-for-purpose decisions. This approach moves beyond traditional metadata by integrating evidence of how data is actually consumed and validated within scientific communities.
Key insights
- Data trust is a multi-dimensional concept that is best assessed by examining its 'social life' and usage patterns within scientific communities, drawing parallels to social trust networks.
- AI-enabled scientific discovery necessitates an evidence-based approach to data selection, moving beyond static metadata to incorporate 'revealed practice' for informed judgments of utility and reliability.
- Systematically converting scattered traces of data usage into structured, auditable graphs can provide crucial, machine-interpretable evidence for evaluating data's fitness for specific scientific purposes.
Summary by alphaXiv.
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