Towards Traceability in Data Ecosystems using a Bill of Materials Model
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Abstract (arXiv)
Researchers and scientists use aggregations of data from a diverse combination of sources, including partners, open data providers and commercial data suppliers. As the complexity of such data ecosystems increases, and in turn leads to the generation of new reusable assets, it becomes ever more difficult to track data usage, and to maintain a clear view on where data in a system has originated and makes onward contributions. Reliable traceability on data usage is needed for accountability, both in demonstrating the right to use data, and having assurance that the data is as it is claimed to be. Society is demanding more accountability in data-driven and artificial intelligence systems deployed and used commercially and in the public sector. This paper introduces the conceptual design of a model for data traceability based on a Bill of Materials scheme, widely used for supply chain traceability in manufacturing industries, and presents details of the architecture and implementation of a gateway built upon the model. Use of the gateway is illustrated through a case study, which demonstrates how data and artifacts used in an experiment would be defined and instantiated to achieve the desired traceability goals, and how blockchain technology can facilitate accurate recordings of transactions between contributors.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2019.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 4; 2025: 1.
12 papers.
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- 2022Reward Reports for Reinforcement Learningcited by 18
- 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Facecited by 6
- 2021A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributionscited by 5
- 2021Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentialscited by 3
- 2024Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Developmentcited by 2
- 2024LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliancecited by 2
- 2023An investigation of licensing of datasets for machine learning based on the GQM modelcited by 1
- 2024Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approachcited by 1
- 2024Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotatecited by 0
- 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face6 Feb 2025
- 2024LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance30 Dec 2024
- 2024Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development16 Nov 2024
- 2024Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach16 Aug 2024
- 2024Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate10 Jun 2024
- 2023An investigation of licensing of datasets for machine learning based on the GQM model24 Mar 2023
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering12 Sep 2022
- 2022Reward Reports for Reinforcement Learning22 Apr 2022
- 2022Towards a Roadmap on Software Engineering for Responsible AI9 Mar 2022
- 2021"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?5 Jul 2021
- 2021Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials13 May 2021
- 2021A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions5 Mar 2021