Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets
Version
Published
Date Issued
2020
Author(s)
Editor(s)
Fiedewald, Michael
Önen, Melek
Lievens, Eva
Krenn, Stephan
Fricker, Samuel
Type
Conference Paper
Language
English
Abstract
High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.
Subjects
QA75 Electronic computers. Computer science
ISBN
978-3-030-42503-6
Publisher DOI
Series/Report No.
IFIP Advances in Information and Communication Technology
Organization
Volume
576
Conference
14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School: Privacy and Identity Management: Data for Better Living: AI and Privacy: Revised Selected Papers
Publisher
Springer International Publishing
Submitter
Gygli, Marcel
Citation apa
Lüthi, P., Gagnaux, T., & Gygli, M. (2020). Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets. In M. Fiedewald, M. Önen, E. Lievens, S. Krenn, & S. Fricker (Eds.), Privacy and Identity Management: Data for Better Living: AI and Privacy (Vol. 576, pp. 411–426). Springer International Publishing. https://arbor.bfh.ch/handle/arbor/42087
