A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions

TL;DR

This paper introduces a new metric to quantify transparency in AI development pipelines, enhancing trust and validation by making contributions more visible and fostering better documentation in scientific AI models.

Contribution

It proposes a novel, quantifiable transparency metric for AI pipelines and evaluates its effectiveness on popular scientific model repositories.

Findings

The metric helps assess transparency levels in AI systems.

Applying the metric promotes better documentation and contribution visibility.

The approach is effective on models from ModelHub and PyTorch Hub.

Abstract

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quickly become problematic, particularly where guidance or regulations change and once-acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate.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.