Introduce an auditing layer to web science

Conference item

Scientific discoveries increasingly depend on data and data processing, and Web Science is no exception. As an established practice, data-intensive research typically uses scientific workflows and provenance to facilitate data and method sharing while automatically preserving processing history. Prior research has reported the possibility of ex-post policy-based compliance checking from provenance data. Based on these works, in this paper, we present the conceptual design of a framework of data-harvesting Web Science practices, especially by introducing a common auditing layer. We discuss the framework’s practical, scientific, and ethical advantages, including its applicability in the period of large language model (LLM), autonomous agent, and artificial intelligence (AI) explosion. We hope this framework design can incubate a new norm for research practice to be transparent, ethical, and lightweight.

Authors: Zhao, R; Wright, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computing Machinery
• Host title: Websci Companion '25: Companion Publication of the 17th ACM Web Science Conference 2025
• Pages: 49-53
• Publication date: 2025-07-21
• Event title: 17th ACM Web Science Conference (WebSci 2025)
• Event location: New Brunswick, NJ, USA
• Event website: https://websci25.org/
• Event start date: 2025-05-20
• Event end date: 2025-05-23
• DOI: 10.1145/3720554.3736184
• ISBN: 9798400715358
• Language: English
• Keywords: Networking and Information Technology R&D (NITRD); data governance; transparency; audit; usage control