  Increased adoption and deployment of machine learning (ML) models into
business, healthcare and other organisational processes, will result in a
growing disconnect between the engineers and researchers who developed the
models and the model's users and other stakeholders, such as regulators or
auditors. This disconnect is inevitable, as models begin to be used over a
number of years or are shared among third parties through user communities or
via commercial marketplaces, and it will become increasingly difficult for
users to maintain ongoing insight into the suitability of the parties who
created the model, or the data that was used to train it. This could become
problematic, particularly where regulations change and once-acceptable
standards become outdated, or where data sources are discredited, perhaps
judged to be biased or corrupted, either deliberately or unwittingly. In this
paper we present a method for arriving at a quantifiable metric capable of
ranking the transparency of the process pipelines used to generate ML models
and other data assets, such that users, auditors and other stakeholders can
gain confidence that they will be able to validate and trust the data sources
and human contributors in the systems that they rely on for their business
operations. The methodology for calculating the transparency metric, and the
type of criteria that could be used to make judgements on the visibility of
contributions to systems are explained and illustrated through an example
scenario.
