Toward a Learning Cycle Data-Governance Toolkit for Rule-Makers and Policy Leaders in Smart Cities

Abstract

This commentary article presents a learning-centric approach to smart cities [FUTURA] with a triadic quality assurance method, inclusive design and use, risk management, and rights protection. The model displays high suitability for contexts of intensive digital transformation, leading to a rise in fairness, social inclusion, and intergenerational sustainability in cities experiencing comprehensive processes of innovation that are data-driven and AI-supported. The article outlines for decision-makers involved in the city governance processes a four-step-based policy design and implementation method at the crossroads of AI, data-driven information, and institutionally embedded knowledge. The proposal takes stock of a longstanding research program delving into the functional and structural rationales of the human oversight (art.14) approach adopted in the context of AI-governance interpenetration. Beyond the empirical evidence gathered through a scientific strategy that has disentangled the mechanisms of digital transformation at the individual, organizational, and systemic levels of four policy sectors, this work heads straightforwardly to make a case in favor of the adoption of a toolkit and the associated methodology in the context of the European AI Act implementation. The empirical field considered is smart cities. This choice merges two rationales, one scientific—the level of analysis of a smart city enables researchers to have a heuristically adequate and in-depth understanding of the three micro-meso-macro interdependences—and one institutional—cities represent nowadays spectacular and highly sensitive targets of innovation, investments, and citizens/leaders' responsiveness and mutual engagement.