As generative AI (GenAI) technologies proliferate in urban governance, the challenge of building trustworthy AI systems becomes increasingly urgent. This chapter critically examines “trustworthiness” not as a purely technical attribute, but as a socio-political construct shaped by power, participation, and policy. Focusing on smart cities as testbeds of algorithmic governance, it explores how decentralized Web3 technologies—such as blockchain, DAOs, and data cooperatives—can offer structural alternatives to centralized, opaque systems. Drawing on action research from the Horizon Europe ENFIELD project and framed by EU policy developments like the AI Act and the Draghi Report, the chapter proposes a multi-layered governance model. It evaluates seven emerging techniques to strengthen GenAI accountability: (i) federated learning, (ii) blockchain provenance tracking, (iii) zero-knowledge proofs, (iv) DAO-based verification, (v) digital watermarking, (vi) explainable AI (XAI), and (vii) privacy-preserving machine learning (PPML). The chapter ultimately argues that trustworthy AI must be embedded in participatory governance, algorithmic transparency, and plural civic oversight. By reframing trust as a relational, institutional, and democratic issue, it contributes to reimagining smart cities not as technocratic projects, but as inclusive arenas for data justice and democratic renewal.