Big Data Cogn. Comput. 2025, 9, 62 https://doi.org/10.3390/bdcc9030062 Article Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems Igor Calzada 1,2,3,4,5,6,7, *, Géza Németh 7 and Mohammed Salah Al-Radhi 7 1 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 2 Basque Foundation for Science, Ikerbasque, Plaza Euskadi 5, 48009 Bilbao, Spain 3 Wales Institute of Social and Economic Research and Data (WISERD), School of Social Sciences, Social Sci- ence Research Park (Sbarc/Spark), Cardiff University, Maindy Road, Cathays, Cardiff CF24 4HQ, UK 4 Decentralization Research Centre, 545 King St. W, Toronto, ON W5V 1M1, Canada 5 Fulbright Scholar-In-Residence (S-I-R), US-UK Fulbright Commission, Unit 302, 3rd Floor Camelford House, 89 Albert Embankment, London SE1 7TP, UK 6 Astera Institute, 2625 Alcatraz Ave #201, Berkeley, CA 94705, USA 7 Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, ENFIELD Horizon, BEM, 1117 Budapest, Hungary * Correspondence: igor.calzada@ehu.eus; Tel.: +34-630-752876 Abstract: As generative AI (GenAI) technologies proliferate, ensuring trust and transpar- ency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decen- tralized 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 con- ducted 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 ad- dressing the socio-political challenges of AI-driven misinformation. Ultimately, this re- search contributes to the development of resilient democratic systems in an era of increas- ing technopolitical polarization. Keywords: generative AI; decentralization; Web3; trustworthy AI; blockchain; DAOs; data cooperatives; big data; detection techniques; democracy 1. Introduction: Trustworthy AI for Whom? The rise of generative artificial intelligence (GenAI) has introduced transformative tools capable of generating complex, human-like content in text, imagery, and sound [1,2]. While these technologies hold vast potential for innovation across industries, they also pose significant risks related to trust, authenticity, and accountability. As the European Academic Editor: Domenico Ursino Received: 10 January 2025 Revised: 17 February 2025 Accepted: 1 March 2025 Published: 6 March 2025 Citation: Calzada, I.; Németh, G.; Al-Radhi, M.S. Trustworthy AI for Whom? GenAI Detection Techniques of Trust Through Decentralized Web3 Ecosystems. Big Data Cogn. Comput. 2025, 9, 62. hps://doi.org/10.3390/bdcc9030062 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Swiꜩerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Aribution (CC BY) license (hps://creativecommons.org/license s/by/4.0/).