AI and big data policing for urban safety : new quantification regimes, market diversification, and reconfiguration of crime prevention – IAAP

More and more Artificial Intelligence (AI) innovations affect key social activities, such as policing. Smart CCTV, facial recognition, or predictive cartography are supposed to help us to build « safe cities ». Beyond promises conveyed by such innovations, this research project aims to measure the concrete effects of AI on police work. This project investigate three main issues :

(1) do algorithmic procedures change the ways of understanding and representing crime phenomena ? do they foster the « scientifization » of police work ? is there a shift from information police to big data policing ?
(2) do algorithmic devices lead urban security policies towards predictive action or do they rather rationalize and control the field policemen work ?
(3) do the controversy raised regarding civil liberties tend to push AI-scientists to integrate socio-political stakes into their technical production ?

To do so, this project research consider the three following dimensions : scientific achievements in AI, how industry (private security, IT firms and urban services) get to grips with those findings, and how it is used to renew urban security policies. The way local public authorities are restructured is also considered.

Methodologically, this project brings together sociology and computer sciences. Three workpackages organize the research work. First, social scientists of the research team will conduct interviews and observation of the main actors from our four case studies. It is important to grasp how AI innovations transform police organization and activity : which part of the police work is automated, and how does it affect professional identities ? Secondly, we will objectify the relationships between scientific, industrial and police actors, through the observation of professional shows and exhibitions, and a network analysis. Finally, computer scientists of the research team will use the sociological results as a non-numerical data in order to correct and complete numerical data (coming from police records or captors such as CCTV). Building on XAI approach (eXplainable Artificial Intelligence), the project aims to better contextualize data and identify possible biases. The project will improve police reflexivity concerning its own activity, by designing an AI model based on collaborative, unbiased and explainable machine learning.

By bringing sociology and computer sciences together, this project therefore seeks to contribute to the better understanding of our social life "algorithmization", which significantly reconfigures how our societies produce knowledge about themselves and govern themselves.