NFT-based Data Provenance for AI Transparency in Enterprise Information Systems
Yiannis Verginadis (Institute of Communication and Computer Systems, Athens University of Economics and Business)
Orestis Almpanoudis (Institute of Communication and Computer Systems)
Dimitris Apostolou (University of Piraeus)
Gregoris Mentzas (Institute of Communication and Computer Systems)
Marcela Tuler De Tuler de Oliveira (TU Delft - Information and Communication Technology)
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Abstract
Enterprise Information Systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain.
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