# Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach

arXiv: https://arxiv.org/abs/2408.08536 · HTML: https://mlsanity.com/paper/2408.08536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://mlsanity.com/paper/1904.04253/) (2019)
- [Trust in Software Supply Chains: Blockchain-Enabled SBOM and the AIBOM Future](https://mlsanity.com/paper/2307.02088/) (2023)
- [An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead](https://mlsanity.com/paper/2301.05362/) (2023)
- [BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems](https://mlsanity.com/paper/2309.12206/) (2023)
- [AIOps for a Cloud Object Storage Service](https://mlsanity.com/paper/2005.03094/) (2020)
- [On the Way to SBOMs: Investigating Design Issues and Solutions in Practice](https://mlsanity.com/paper/2304.13261/) (2023)
- [Tag Your Fish in the Broken Net: A Responsible Web Framework for Protecting Online Privacy and Copyright](https://mlsanity.com/paper/2310.07915/) (2023)

## Built on by (most cited first, 1 of 1)

- [RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2506.03401/) (2025)

## Abstract (arXiv)

In the era of advanced artificial intelligence, highlighted by large-scale generative models like GPT-4, ensuring the traceability, verifiability, and reproducibility of datasets throughout their lifecycle is paramount for research institutions and technology companies. These organisations increasingly rely on vast corpora to train and fine-tune advanced AI models, resulting in intricate data supply chains that demand effective data governance mechanisms. In addition, the challenge intensifies as diverse stakeholders may use assorted tools, often without adequate measures to ensure the accountability of data and the reliability of outcomes. In this study, we adapt the concept of ``Software Bill of Materials" into the field of data governance and management to address the above challenges, and introduce ``Data Bill of Materials" (DataBOM) to capture the dependency relationship between different datasets and stakeholders by storing specific metadata. We demonstrate a platform architecture for providing blockchain-based DataBOM services, present the interaction protocol for stakeholders, and discuss the minimal requirements for DataBOM metadata. The proposed solution is evaluated in terms of feasibility and performance via case study and quantitative analysis respectively.