Camilla Quaresmini

Policy frameworks to promote the diffusion of sustainable mobility technologies are increasingly moving toward a data-driven approach. While they investigate incentive policies to foster the widespread diffusion of sustainable mobility technologies leveraging different data sources, these frameworks often fail to include issues of fairness. However, the acceptance of sustainable mobility technologies implies radical changes in the everyday life: besides transport habits, these changes are potentially hampered by socioeconomic individual features, that can raise potential discrimination. Focusing on a data-driven example of a social network of agents, we study how new mobility habits spread based on a combination of personal attitudes and mutual influence. Considering the epistemic dimension of the network, we address a specific type of discrimination, namely, the exclusion of some agents from their epistemic connections due to their own social features. We thus introduce the notion of epistemic fairness and consider it in the adoption dynamics. Our approach merges theoretical analysis with technical tools, jointly making them active means to create a preliminary model for exploring and analyzing different scenarios. The ultimate goal is to include elements of fairness in the design of the data-driven models supporting the creation of policy processes aiming at reducing inequalities within the context of sustainable mobility diffusion.