Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets
This addresses the problem of fostering collaboration between actors in AI value chains by improving transparency and trust, though it is incremental as it builds on existing blockchain and provenance concepts.
The paper tackles the problem of trust and control in sharing AI assets by designing a graph-based provenance model and secure exchange protocol, implemented as a smart contract on a blockchain to enable tracing in an industry use case, solving challenges like privacy and traceability.
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.