Dissertation defence (Law): LL.M., MSc Carlo Gatti
LL.M., MSc Carlo Gatti defends the dissertation in Law titled “Theoretical Roots, Rationalisations, and Legal Contradictions of Predictive Policing: Reflections from the Italian Case” at the University of Turku on 7 November 2025 at 12.15 (University of Turku, Calonia, Cal1 lecture hall, Caloniankuja 3, Turku).
Opponent: Associate Professor, PhD Simone Tulumello (Instituto de Ciências Sociais – University of Lisbon, Portugal)
Custos: Professor, PhD Anne Alvesalo-Kuusi (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-952-02-0355-9
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Summary of the Doctoral Dissertation:
Predictive policing refers to the use of algorithms to forecast future crimes or crime locations. In my doctoral research, I critically examine this practice by focusing on Italy as a case study. Although predictive policing is often presented as a neutral tool for crime prevention, my work reveals that it is shaped by deeper political and economic choices, rooted in long-standing criminological theories and institutional agendas. To explore this, I analyse how predictive systems are influenced by historical models that link crime to poverty and urban space. These systems typically rely on past crime data and socio-economic indicators, which tend to reinforce existing inequalities. For this reason, I challenge the widespread belief that place-based predictions are less intrusive than person-based ones. In fact, both approaches rely on similar data and ideological assumptions.
By tracing the continuity between spatial and individualised crime prediction, I show how both traditions converge in targeting marginalised populations. Building on this theoretical framework, I conducted fieldwork to reconstruct the workings of two predictive tools currently used in Italy. This investigation uncovered legal gaps, regulatory shortcomings, and serious transparency issues. In particular, I argue that secrecy around algorithmic inputs prevents meaningful public oversight and undermines basic legal principles, such as the presumption of innocence and the right to anticipate institutional reactions. Moreover, I highlight how private companies increasingly influence public policing, often without formal accountability—a process I define as “soft privatisation”.
This research contributes to exposing the hidden politics behind predictive technologies. Rather than offering technical fixes, it proposes a materialist critique that connects predictive policing to broader patterns of social control and penal ivity. In doing so, it adds to ongoing debates on algorithmic governance, human rights, and the future of law enforcement in digital societies.
Opponent: Associate Professor, PhD Simone Tulumello (Instituto de Ciências Sociais – University of Lisbon, Portugal)
Custos: Professor, PhD Anne Alvesalo-Kuusi (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-952-02-0355-9
***
Summary of the Doctoral Dissertation:
Predictive policing refers to the use of algorithms to forecast future crimes or crime locations. In my doctoral research, I critically examine this practice by focusing on Italy as a case study. Although predictive policing is often presented as a neutral tool for crime prevention, my work reveals that it is shaped by deeper political and economic choices, rooted in long-standing criminological theories and institutional agendas. To explore this, I analyse how predictive systems are influenced by historical models that link crime to poverty and urban space. These systems typically rely on past crime data and socio-economic indicators, which tend to reinforce existing inequalities. For this reason, I challenge the widespread belief that place-based predictions are less intrusive than person-based ones. In fact, both approaches rely on similar data and ideological assumptions.
By tracing the continuity between spatial and individualised crime prediction, I show how both traditions converge in targeting marginalised populations. Building on this theoretical framework, I conducted fieldwork to reconstruct the workings of two predictive tools currently used in Italy. This investigation uncovered legal gaps, regulatory shortcomings, and serious transparency issues. In particular, I argue that secrecy around algorithmic inputs prevents meaningful public oversight and undermines basic legal principles, such as the presumption of innocence and the right to anticipate institutional reactions. Moreover, I highlight how private companies increasingly influence public policing, often without formal accountability—a process I define as “soft privatisation”.
This research contributes to exposing the hidden politics behind predictive technologies. Rather than offering technical fixes, it proposes a materialist critique that connects predictive policing to broader patterns of social control and penal ivity. In doing so, it adds to ongoing debates on algorithmic governance, human rights, and the future of law enforcement in digital societies.
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