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

TL;DR

This paper introduces a blockchain-based Data Bill of Materials (DataBOM) to enhance traceability, accountability, and governance of datasets in AI data supply chains, addressing challenges of complexity and stakeholder diversity.

Contribution

It adapts the Software Bill of Materials concept to data governance, proposing a blockchain-enabled platform and interaction protocol for managing dataset dependencies and metadata.

Findings

Demonstrates feasibility through a case study

Evaluates performance with quantitative analysis

Shows improved data traceability and accountability

Abstract

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.