Content area

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.

Details

1009240
Company / organization
Title
Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems
Author
Calzada, Igor 1 ; Németh, Géza 2 ; Mohammed Salah Al-Radhi 2 

 Public Policy & Economic History Department, Faculty of Economy and Business, University of the Basque Country, UPV-EHU, Oñati Square 1, 20018 Donostia-San Sebastián, Spain; Basque Foundation for Science, Ikerbasque, Plaza Euskadi 5, 48009 Bilbao, Spain; Wales Institute of Social and Economic Research and Data (WISERD), School of Social Sciences, Social Science Research Park (Sbarc/Spark), Cardiff University, Maindy Road, Cathays, Cardiff CF24 4HQ, UK; Decentralization Research Centre, 545 King St. W, Toronto, ON W5V 1M1, Canada; Fulbright Scholar-In-Residence (S-I-R), US-UK Fulbright Commission, Unit 302, 3rd Floor Camelford House, 89 Albert Embankment, London SE1 7TP, UK; Astera Institute, 2625 Alcatraz Ave #201, Berkeley, CA 94705, USA; Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, ENFIELD Horizon, BEM, 1117 Budapest, Hungary 
 Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, ENFIELD Horizon, BEM, 1117 Budapest, Hungary 
Publication title
Volume
9
Issue
3
First page
62
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
e-ISSN
25042289
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-03-06
Milestone dates
2025-01-10 (Received); 2025-03-01 (Accepted)
Publication history
 
 
   First posting date
06 Mar 2025
ProQuest document ID
3181353869
Document URL
https://www.proquest.com/scholarly-journals/trustworthy-ai-whom-genai-detection-techniques/docview/3181353869/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-10
Database
ProQuest One Academic