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Increased adoption and deployment of machine learning (ML) models intobusiness, healthcare and other organisational processes, will result in agrowing disconnect between the engineers and researchers who developed themodels and the model's users and other stakeholders, such as regulators orauditors. This disconnect is inevitable, as models begin to be used over anumber of years or are shared among third parties through user communities orvia commercial marketplaces, and it will become increasingly difficult forusers to maintain ongoing insight into the suitability of the parties whocreated the model, or the data that was used to train it. This could becomeproblematic, particularly where regulations change and once-acceptablestandards become outdated, or where data sources are discredited, perhapsjudged to be biased or corrupted, either deliberately or unwittingly. In thispaper we present a method for arriving at a quantifiable metric capable ofranking the transparency of the process pipelines used to generate ML modelsand other data assets, such that users, auditors and other stakeholders cangain confidence that they will be able to validate and trust the data sourcesand human contributors in the systems that they rely on for their businessoperations. The methodology for calculating the transparency metric, and thetype of criteria that could be used to make judgements on the visibility ofcontributions to systems are explained and illustrated through an examplescenario.

Quantifying Transparency of Machine Learning Systems through Analysis of Contributions