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

arXiv: https://arxiv.org/abs/2103.03610 · HTML: https://mlsanity.com/paper/2103.03610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 71 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [An introduction to Docker for reproducible research, with examples from the R environment](https://mlsanity.com/paper/1410.0846/) (2014)
- [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://mlsanity.com/paper/1904.04253/) (2019)
- [Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform](https://mlsanity.com/paper/1810.07159/) (2018)
- [Identifying Unmaintained Projects in GitHub](https://mlsanity.com/paper/1809.04041/) (2018)
- [ModelHub.AI: Dissemination Platform for Deep Learning Models](https://mlsanity.com/paper/1911.13218/) (2019)
- [Defending against Sybil Devices in Crowdsourced Mapping Services](https://mlsanity.com/paper/1508.00837/) (2015)
- [Measurement in AI Policy: Opportunities and Challenges](https://mlsanity.com/paper/2009.09071/) (2020)
- [Certifying Provenance of Scientific Datasets with Self-sovereign Identity and Verifiable Credentials](https://mlsanity.com/paper/2004.02796/) (2020)
- [A Conceptual Architecture for Contractual Data Sharing in a Decentralised Environment](https://mlsanity.com/paper/1904.03045/) (2019)

## Built on by (most cited first, 5 of 5)

- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)
- [Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials](https://mlsanity.com/paper/2105.06370/) (2021)
- [Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability](https://mlsanity.com/paper/2310.08849/) (2023)
- [Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics](https://mlsanity.com/paper/2403.18838/) (2024)

## Abstract (arXiv)

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.