On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda
Open accessAbstract
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 untrustworthy environments. In recent research, both technologies are used in similar and even the same systems. This can lead to a convergence of AI and DLT, which in the past, has paved the way for major innovations of other information technologies. Previous work highlights several potential benefits of a convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. In this research, we review and synthesize extant research on integrating AI with DLT and vice versa to rigorously develop a future research agenda on the convergence of both technologies. In terms of integrating AI with DLT, we identified research opportunities in the areas of secure DLT, automated referee and governance, and privacy-preserving personalization. With regard to integrating DLT with AI, we identified future research opportunities in the areas of decentralized computing for AI, secure data sharing and marketplaces, explainable AI, and coordinating devices. In doing so, this research provides a four-fold contribution. First, it is not constrained to blockchain but instead investigates the broader phenomenon of DLT. Second, it considers the reciprocal nature of a convergence of AI and DLT. Third, it bridges the gap between theory and practice by helping 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. Fourth, it demonstrates the feasibility of applying the convergence concept to research on AI and DLT.
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The growing availability of training data is a fundamental reason for recent advancements of AI.
“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).”
From Trustworthy artificial intelligence (2021) · cites this paper as Pandl et al. 2020
Most research prototypes employ DLT-based federated learning to train relatively simple AI models, and extant research indicates a performance overhead of 5% to 15%.
“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).”
From Trustworthy artificial intelligence (2021) · cites this paper as Pandl et al. 2020
There is a nascent stream of literature combining DLT with AI to address diverse issues of current AI-based systems.
“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).”
From Trustworthy artificial intelligence (2021) · cites this paper as Pandl et al. 2020
In federated learning, no input training data is directly shared; partial AI models are trained by nodes, with provenance and integrity preserved using a distributed ledger.
“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).”
From Trustworthy artificial intelligence (2021) · cites this paper as Pandl et al. 2020
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Hu, B. A., & Ting, F. (2024) · Halfway to the Future Symposium · doi:10.1145/3686169.3686209 · Cited by 2 · see citing works
Artificial Intelligence in Investment and Wealth Management
Ghosn, F. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0129-7.ch001 · Cited by 2 · see citing works
Jump to platform faster? Gender, institutional change, and pre-entrant entrepreneurial attempt
Xu, L., Parboteeah, K. P., & Fang, H. “. (2023) · New England journal of entrepreneurship · doi:10.1108/neje-06-2022-0040 · Cited by 2 · see citing works
Overmind: A Collaborative Decentralized Machine Learning Framework
Sakul-Ung, P., Vatcharaphrueksadee, A., Ruchanawet, P., Kearpimy, K., Ketmaneechairat, H., & Maliyaem, M. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050634 · Cited by 2 · see citing works
Enhancing corporate governance with decentralized AI: a structuration theory perspective
Jain, G., Singh, H., & Hashimy, L. (2025) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-10-2024-0425 · Cited by 1 · see citing works
Nethrasri, P. (2025) · International Journal of Research in Engineering and Management Sciences · doi:10.65477/ijrems.v1.i1.01 · Cited by 1 · see citing works
Asaad, A. M., Qahtan, M. H., & Younis, A. K. (2026) · International Journal of Computational and Electronic Aspects in Engineering · doi:10.26706/ijceae.7.4.20260815 · Cited by 1 · see citing works
Boosting Sustainability: R&D Mobiliser Projects in Portuguese Ornamental Stone Companies
de Almeida, I. C., & da Silva, A. A. (2024) · European Journal of Applied Business and Management · doi:10.58869/ejabm10(1)/07 · Cited by 1 · see citing works
Resilient Autonomy: A Digital Architecture for Symbiotic Multi-Robot Fleet Management
Mitchell, D., Harper, S., Nandakumar, S., Blanche, J. R. D., Lim, T., Imran, M. A., Taha, A., & Flynn, D. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978450 · Cited by 1 · see citing works
Dynamic Identity and Access Management in the IoT: Blockchain‐based Approach
Mendiboure, L., Chalouf, M. A., & Krief, F. (2022) · Wiley eBooks · doi:10.1002/9781394156030.ch9 · Cited by 1 · see citing works
Vertrauenswürdige Systeme mit künstlicher Intelligenz
Sunyaev, A. (2020) · Wirtschaftsinformatik & Management · doi:10.1365/s35764-020-00250-6
Self-citationFraga‐Lamas, P., & Fernández‐Caramés, T. M. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.101389
Implementation of E-Health Services Using Machine Learning for Smart Cities
Joshi, M., Tewari, N., & Budhani, S. K. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9710-1.ch007
Blockchain used in apparel supply and textile
Chaubey, C. (2023) · AIP conference proceedings · doi:10.1063/5.0116490
AI verification platform using block chain with distributed ledger technology (DLT)
Sharma, S. K. (2023) · AIP conference proceedings · doi:10.1063/5.0101178
Künstliche Intelligenz in der Fertigung
Chen, T., & Wang, Y.-C. (2023) · Springer eBooks · doi:10.1007/978-3-031-44280-3_2
- Authors
- Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., & Sunyaev, A.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 57075-57095
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3003473443
- Cited by
- 115
- References
- 121
- Citations by year
- 2026: 8 · 2025: 27 · 2024: 21 · 2023: 22 · 2022: 14 · 2021: 17 · 2020: 6
- FWCI
- 13.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Repository KITopen (Karlsruhe Institute of Technology) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- KITopen (cc-by)
- Funders
- Karlsruhe Institute of Technology through the KIT-Publication Fund
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, distributed ledger technology, blockchain, technology convergence, algorithmic governance, privacy-preserving personalization, decentralized computing, secure data sharing, explainable AI, consensus, future research agenda
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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