ARTIFICIAL INTELLIGENCE IN PUBLIC WELFARE POLICIES: ADOPTIONS, PERCEPTIONS AND TRUST

What consequences will the widespread adoption of Artificial Intelligence (AI) in the public sector have on society? I was inspired by this question to conduct five studies using multidisciplinary and multi-methods research designs. While most research focuses on the private sector or pioneering countries in digitalization and public services, this work examines automated decision-making tools (ADM) and AI in Italian public welfare policies. Firstly, using qualitative methods, I explore how public employees perceive that AI adoption in public welfare policies can affect organizational and social factors such as administrative discretion and social inclusion. Secondly, relying on quantitative and experimental methods I examine two further social consequences: trust in AI applications and trust in institutions adopting AI. Besides advancing knowledge about this specific topic in Italy, the overall theoretical contribution highlights the role of social factors— including public values, social norms, and prior experience with institutions— in shaping the consequences of AI adoption rather than the technical characteristics of algorithms. Methodologically, this thesis generates a new dataset that combines survey items with experimental measures of trust in institutions and attitudes toward AI in Italy. Results from the qualitative study indicate that in Italy a fragmented and heterogenous AI adoption is occurring in the provision of welfare benefits and to detect fraudulent behaviours. These adoptions already imply relevant organizational and social consequences, reinforcing public employees’ narratives of efficiency in terms of redistribution, social inclusion and the relation between the state and citizens. Survey items reveal balance between AI attitudes such as perceived benefits and risks, but contradictory patterns concern AI knowledge and awareness. Moreover, perceived benefits and social norms consistently predict higher trust in AI applications. Instead, perceived risks play a more heterogeneous role, particularly for high-stake domains such as healthcare or human resources compared to Chat-GPT like applications. Finally, a vignette experiment indicates that institutional trust is still shaped more by performance and prior experience than the decision-maker— whether a public administrator, a hybrid system or an AI. However, context-dependent dynamics emerge, particularly between welfare-related and justice-related decisions.