Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems
Open accessPublished version of a preprint
Abstract
As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe Lighthouse project called ENFIELD: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) zero-knowledge proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) privacy-preserving machine learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Batty, M. (2013) · doi:10.7551/mitpress/9399.001.0001 · Cited by 1,700 · see citing works
The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences
Kitchin, R. (2014) · doi:10.4135/9781473909472 · Cited by 1,469 · see citing works
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Luccioni, S., Jernite, Y., & Strubell, E. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658542 · Cited by 392 · see citing works
Scalable watermarking for identifying large language model outputs
Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., et al. (2024) · Nature · doi:10.1038/s41586-024-08025-4 · Cited by 132 · see citing works
Lazar, S., & Nelson, A. (2023) · Science · doi:10.1126/science.adi8982 · Cited by 77 · see citing works
Digital authoritarianism: a systematic literature review
Roberts, T., & Oosterom, M. (2025) · Information Technology for Development · doi:10.1080/02681102.2024.2425352 · Cited by 74 · see citing works
Repression in the Digital Age: Surveillance, Censorship, and the Dynamics of State Violence
Gohdes, A. R. (2024) · Oxford University Press · doi:10.1093/oso/9780197743577.001.0001 · Cited by 59 · see citing works
Kolt, N. (2024) · doi:10.2139/ssrn.4772956 · Cited by 47 · see citing works
Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents
Bousetouane, F. (2025) · doi:10.32388/2dkdck · Cited by 29 · see citing works
AI as a constituted system: accountability lessons from an LLM experiment
Nabben, K. (2024) · Data & Policy · doi:10.1017/dap.2024.58 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
ADABARA, I., Olaniyi Sadiq, B., Nuhu Shuaibu, A., Ibrahim Danjuma, Y., & Maninti, V. (2025) · F1000Research · doi:10.12688/f1000research.169927.1 · Cited by 38 · see citing works
Digital inclusion and Urban AI: strategic roadmapping and policy challenges
Calzada, I., & Eizaguirre, I. (2025) · Discover Cities · doi:10.1007/s44327-025-00116-9 · Cited by 15 · see citing works
“Taken together, these six cases show that trustworthy AI is not a technical property—it is a political achievement [28] .”
Self-citationDagtekin, U., & Kabakuş, A. K. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0161218 · Cited by 7
“…that lie at the heart of CRT. Similarly, research centering epistemic trust [74] shows that cognitive filters toward information sources determine trust in ChatGPT, while work examining transparency and ethical signals [15] demonstrates that such cues reduce counter-arguments generated by users.”
AI driven system for enhancing consumer electronics through maintenance personalization and security
Simaiya, S., Singh, V., Challa, P., Sharma, K., Pandurangan, S., Tekeste, L. G., Ghith, E., Hussien, S. A., & Lilhore, U. (2026) · Scientific Reports · doi:10.1038/s41598-026-37401-5 · Cited by 3
“Traditional algorithms are typically static and non-adaptive, often relying on low-dimensional or uncorrelated data sources that cannot capture dynamic device behaviour 13,28 .”
Generative AI and the urban AI policy challenges ahead: Trustworthy for whom?
Calzada, I. (2025) · Transforming Government: People, Process and Policy · doi:10.1108/tg-08-2025-0240 · Cited by 3
Self-citationUğur, S. (2026) · E-Learning and Digital Media · doi:10.1177/20427530261449468 · Cited by 1 · see citing works
The Decision Path to Control AI Risks Completely: Fundamental Control Mechanisms for AI Governance
Tao, Y. (2025) · arXiv.org · doi:10.48550/arxiv.2512.04489 · Cited by 1
“Further complicating gAI governance is its ability to autonomously generate emergent content, raising fundamental questions about the reliability of truth and the maintenance of trust in the age of AI [105].”
AI still has a vocabulary problem: toward terminological accountability in artificial intelligence
Hussain, A., Jin, W., & Vincent, N. (2026) · AI and Ethics · doi:10.1007/s43681-026-01447-y
“One work [ 51 ] connects “trust-worthy AI” to the EU’s AI Act, arguing that as the European Commission pushes to assure “trustworthy AI”, clarifying for whom it should be trustworthy and for what purposes becomes increasingly urgent.”
Conceptualizations and Boundaries of EU Digital Sovereignty
Macpherson, L., & Veit, D. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105210
Ahmed, S., Hale, B. J., & Thompson, W. (2026) · Computers in Human Behavior · doi:10.1016/j.chbah.2026.100394
Beyond Digitization Blockchain Data Governance for Trustworthy Academic Ecosystems
Safitri, H., & Safitri, E. D. (2026) · Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi · doi:10.33050/mentari.v5i1.1129
“Temuan tersebut juga mengindikasikan bahwa teknologi blockchain memberikan pengaruh secara langsung terhadap tata kelola data serta turut berkontribusi terhadap terbentuknya ekosistem akademik yang terpercaya [48].”
Kummari, D., Burugulla, J. K. R., Malempati, M., Amistapuram, K., Garapati, R., & Nagabhyru, K. C. (2025) · International Conference Communication, Networks & Computing · doi:10.1109/cnc68716.2025.11484989
Rajwade, S., Yadaw, P. K., & Ingle, A. (2025) · Lex Localis-journal of Local Self-government · doi:10.52152/801158
“Triangulation tries to balance academic literature results, industry reports with regulatory findings to find out any bias [9].”
R, J., Seerapu, V., C., K. D., Gayatri, S. N., Narayana, M., & Penubaka, K. K. R. (2025) · Journal of Neonatal Surgery · doi:10.52783/jns.v14.2792
“In mental health, SVM has the ability to classify patient populations based on symptomatology, treatment outcome, and sentiment analysis [7].”
GenAI-Content Explainable Detection System
Moore, L., & Olszewska, J. (2026) · International Conference on Enterprise Information Systems · doi:10.5220/0014974400004018
“…increase (Wang et al., 2021) with more potential yet to explore, whereas it brought new challenges in terms of digital ecosystem trustworthiness (Calzada et al., 2025), data privacy (Michel-Villarreal et al., 2023), intellectual property rights (Hern, A., 2022) (Ferrara, 2024), information…”
- Authors
- Calzada, I., Németh, G., & Al-Radhi, M. S.
- Affiliations
- Ikerbasque (ES)
- University of the Basque Country (ES)
- Fulbright Commission (GB)
- Corresponding author
- Calzada, Igor
- Venue
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Published
- 2025-03-06
- Volume
- 9
- Issue
- 3
- Pages
- 62
- ISSN
- 2504-2289 (ISSN-L)
- OpenAlex
- W4408186450
- Cited by
- 16
- References
- 145
- Citations by year
- 2026: 8 · 2025: 8
- FWCI
- 16.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data and Cognitive Computing (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission, Horizon 2020, H2020-MSCA-COFUND-2020-101034228-WOLFRAM2: Ikerbasque Start Up Fund (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- UPV-EHU, Research Groups (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Presidency of the Basque Government, External Affairs General Secretary, Basque Communities Abroad Direction, Scientific Direction and Strategic Advisory e-Diaspora Platform HanHemen (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Ayuda en Acción NGO, Innovation & Impact Unit, Research Contract: Scientific Direction and Strategic Advisory (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- European Commission, Horizon Europe, ENFIELD-European Lighthouse to Manifest Trustworthy and Green AI, HORIZON-CL4-2022-HUMAN-02-02-101120657 (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Gipuzkoa Province Council, Etorkizuna Eraikiz 2024: AI’s Social Impact in the Historical Province of Gipuzkoa (AI4SI) (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Warsaw School of Economics SGH (Poland) by RID LEAD, Regional Excellence Initiative Programme (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- SOAM Residence Programme: Network Sovereignties (Germany) via BlockchainGov (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Decentralization Research Centre (Canada) (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- The Learned Society of Wales (LSW) (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Fulbright Scholar-In-Residence (S-I-R) (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- US–UK Fulbright Commission and IIE, US Department of State at the California State University (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Economic and Social Research Council (ESRC) (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- WISERD Civil Society: Changing Perspectives on Civic Stratification/Repair (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Gipuzkoa Province Council, Human Rights & Democratic Culture: Gipuzkoa Algorithmic Territory: Socially Cohesive Digitally Sustainable? And Digital Inclusion & Generative AI International Summer School Scientific Direction, 15–16 July 2025, Donostia-St. Sebastian, Spain (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Astera Institute, Cosmik Data Cooperatives for Open Science (3021.23.EMAJ, IT 1441-22, IT 1541-22, PT10859, SGA oc1-2024-TES-01-01, 2024-LAB2-007-01, 524205, 2022-23, PS00334379, ES/S012435/1, PT10937)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI detection, trustworthy AI, Web3, blockchain, decentralized autonomous organizations, data cooperatives, AI-generated misinformation, EU AI Act, Draghi report, federated learning, zero-knowledge proofs, digital watermarking, explainable AI, privacy-preserving machine learning, data sovereignty, AI governance, democratic regimes, content authenticity
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review