Ambition, Capacity and Reality: The Use of Artificial Intelligence in the Public Sector in Shanghai

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dc.contributor.advisor Cavalieri, Renzo Riccardo it_IT
dc.contributor.author Todaro, Diego <1982> it_IT
dc.date.accessioned 2023-03-13 it_IT
dc.date.accessioned 2024-02-21T12:05:56Z
dc.date.issued 2023-06-09 it_IT
dc.identifier.uri http://hdl.handle.net/10579/25064
dc.description.abstract This dissertation examines whether Shanghai is fulfilling its goal to improve public service provision by accelerating the use of artificial intelligence (AI) in the public sector. After clarifying the technical and social factors affecting the use of AI in this area, it gauges the capacity of Shanghai to implement AI in the public sector. Then it examines how this capacity has translated into three policy initiatives that promote public sector AI applications, the strengths and limitations of such initiatives, and their impacts on public service provision in the municipality. Upon this basis, the analysis demonstrates that Shanghai is enhancing public service delivery by integrating AI in the activities of public organizations. By using the findings of the study of Shanghai to gain a deeper understanding of key topics in public sector AI research, this dissertation contributes new knowledge about the use of AI to improve the provision of public services. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Diego Todaro, 2023 it_IT
dc.title Ambition, Capacity and Reality: The Use of Artificial Intelligence in the Public Sector in Shanghai it_IT
dc.title.alternative Ambition, Capacity and Reality: The Use of Artificial Intelligence in the Public Sector in Shanghai it_IT
dc.type Doctoral Thesis it_IT
dc.degree.name Studi sull'asia e sull'africa it_IT
dc.degree.level Dottorato it_IT
dc.degree.grantor it_IT
dc.description.academicyear D2_appello_10-04-23 it_IT
dc.rights.accessrights embargoedAccess it_IT
dc.thesis.matricno 804445 it_IT
dc.subject.miur SPS/14 STORIA E ISTITUZIONI DELL'ASIA it_IT
dc.description.note Nessuna annotazione it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 2025-02-20T12:05:56Z
dc.provenance.upload Diego Todaro (804445@stud.unive.it), 2023-03-13 it_IT
dc.provenance.plagiarycheck Renzo Riccardo Cavalieri (cavalieri@unive.it), 2023-04-10 it_IT


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