  Increased adoption of artificial intelligence (AI) systems into scientific
workflows will result in an increasing technical debt as the distance between
the data scientists and engineers who develop AI system components and
scientists, researchers and other users grows. This could quickly become
problematic, particularly where guidance or regulations change and
once-acceptable best practice becomes outdated, or where data sources are later
discredited as biased or inaccurate. This paper presents a novel method for
deriving a quantifiable metric capable of ranking the overall transparency of
the process pipelines used to generate AI systems, such that users, auditors
and other stakeholders can gain confidence that they will be able to validate
and trust the data sources and contributors in the AI systems that they rely
on. The methodology for calculating the metric, and the type of criteria that
could be used to make judgements on the visibility of contributions to systems
are evaluated through models published at ModelHub and PyTorch Hub, popular
archives for sharing science resources, and is found to be helpful in driving
consideration of the contributions made to generating AI systems and approaches
towards effective documentation and improving transparency in machine learning
assets shared within scientific communities.
