SpecialityISTI-CNR, Pisa, IT

Social AI: Challenges of the human-AI ecosystemThe increasing prevalence of large-scale socio-technical systems integrating human-AI interactions has opened up new possibilities for collective phenomena and tipping points, accompanied by unforeseen consequences. In this study, we investigate the case of data-informed navigation apps as an example of such systems. While these apps have become indispensable in navigating our urban environments effortlessly, their potential impact on the environment remains poorly understood, raising concerns about their contribution to CO2 emissions and pollution. To address this issue, we introduce a simulation framework aiming to elucidate the influence of routing algorithms on CO2 emissions within urban areas. Our research begins by examining the spatial patterns of urban emissions, uncovering valuable insights that inform the design of effective emissions reduction strategies. Subsequently, we present the results of several simulations that assess the impact of navigation apps on urban emissions, with a specific focus on the role of "route diversification." Through these simulations, we explore how diversifying routes can potentially mitigate the environmental consequences of navigation app usage. By sharing our findings, we aim to provide a foundation for the development of next-generation routing principles that prioritize both individual needs and the well-being of urban environments. This research sheds light on the crucial intersection of AI technologies, sustainable transportation, and urban planning, emphasizing the importance of informed decision-making to foster a harmonious coexistence between humans and technology while minimizing environmental harm.