AI-driven data governance for smart cities: balancing privacy, efficiency, and public trust
Open accessAbstract
The integration of Artificial Intelligence (AI) in smart cities has transformed urban governance, enhancing efficiency in public services, infrastructure management, and decision-making. However, the widespread use of AI for data collection and analysis raises significant challenges related to privacy, algorithmic bias, transparency, and public trust. Without proper governance, AI systems risk exacerbating inequalities, infringing on citizen rights, and reducing accountability in automated decision-making. This paper explores how AI-driven frameworks can enhance data governance while ensuring privacy protection, algorithmic fairness, and citizen empowerment. Key strategies include federated learning to enable decentralized data processing, differential privacy to protect individual identities, and explainable AI to increase transparency in automated decisions. Additionally, bias detection mechanisms and algorithmic audits are essential to prevent discrimination in AI-driven urban systems. Public trust is crucial in smart city initiatives, requiring citizen engagement models, participatory AI councils, and transparent data-sharing policies. Case studies illustrate how open data initiatives, AI-driven services, and digital innovation can enhance public service delivery and civic engagement when guided by strong governance and ethical data management. The paper proposes a comprehensive governance framework integrating privacy-centric AI, fairness-aware algorithms, and public engagement strategies to ensure sustainable, transparent, and accountable AI-driven urban ecosystems. The findings suggest that aligning technological innovation with inclusive policies and capacity-building not only improves urban efficiency and resilience but also builds public trust and empowerment. By aligning technological advancements with ethical and legal safeguards, smart cities can optimize AI’s potential while maintaining public trust and regulatory compliance.
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Algorithmic administration can embed emergency logics into routine decision-making, particularly where data collected for crisis response is later repurposed for control or surveillance.
“Algorithmic administration can embed emergency logics into routine decision-making, particularly where data collected for crisis response is later repurposed for control or surveillance (Mastrogiovanni, 2025; Zuiderveen Borgesius, 2020).”
From Judicial review of algorithmic administrative systems legality evidence and remedies in the smart city state (2026) · cites this paper as Mastrogiovanni, 2025
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Ameen, K. S., Towfiq, T. A., Mousa, K. M., & Pooyanmehr, M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1802986 · Cited by 4 · see citing works
- Authors
- Mastrogiovanni, S.
- Affiliations
- New York University (US)
- Corresponding author
- Mastrogiovanni, Sergio
- Venue
- South Florida Journal of Development
- Publisher
- Brazilian Journals
- Published
- 2025-09-19
- Volume
- 6
- Issue
- 9
- Pages
- e5810
- ISSN
- 2675-5459 (ISSN-L)
- OpenAlex
- W4414374735
- Cited by
- 5
- References
- 5
- Citations by year
- 2026: 5
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- South Florida Journal of Development (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- smart cities, privacy preservation, algorithmic fairness, algorithmic bias, algorithmic transparency, federated learning, differential privacy, explainable AI, algorithmic auditing, bias detection, public trust, citizen participation, open data, citizen empowerment, automated decision-making, accountability, urban governance, AI ethics
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