On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

Abstract

Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in uncertain environments. In recent research, both technologies are used in similar and even the same systems. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major innovations in information technology. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on previous work and providing rigorously derived future research opportunities. This work helps researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies.

Trustworthy artificial intelligence

“…Next to advances in machine learning and deep learning methods, the growing availability of training data represents a fundamental reason for the recent advancements of AI (Pandl et al 2020). However, the creation of high-quality training data is costly and time-consuming, especially when expert knowledge is required (e.g., labeling of thousands of medical images).…”

“…It enables the coordination of economic activity through the creation of secure, transparent, and decentralized electronic markets (Berg et al 2019;Kollmann et al 2019;Subramanian 2017), and is probably best known under the name of blockchain, which is a specific type of DLT (Kannengiesser et al 2020;Sunyaev 2020). Second, there is a nascent stream of literature combining DLT with AI to address diverse issues of current AI-based systems (Pandl et al 2020). The unique combination of DLT's inherent characteristics (e.g., tamper resistance, transparency, and confidentiality) presents it as a promising technical means to address several, albeit not all, of the aforementioned tensions between data at the input and model stages and the outlined TAI principles.…”

“…In such a federated learning scenario, no input training data is directly shared. Instead, partial AI models are being trained by nodes participating in the federated learning network, while training data provenance and the integrity of the partial AI models are preserved using a distributed ledger (Pandl et al 2020). Since no training data is directly shared among participants of the network, DLT-based federated learning seems to be particularly auspicious for addressing the invasion of privacy tensions at the input and model stages.…”

“…However, there remain several issues that warrant further research before we will be able to deploy DLTbased federated learning in real-world use cases. Most research prototypes, for example, employ DLT-based federated learning to train relatively simple AI models (Pandl et al 2020;Preuveneers et al 2018), while extant research indicates that DLT-based federated learning induces a performance overhead of 5% to 15% (Preuveneers et al 2018). Although this might at first not seem like a large overhead, it could ultimately render DLT-based federated learning prohibitively expensive for more complex AI models.…”

“…Artificial intelligence (AI) enables computers to execute tasks that are easy for people to perform but difficult to describe formally (Pandl et al 2020). It is one of the most-discussed technology trends in research and practice today, and estimated to deliver an additional global economic output of around USD 13 trillion by the year 2030 (Bughin et al 2018).…”