Data on, for example, traffic congestion or feelings of (in)safety in certain neighbourhoods is collected by many participants but processed by a single central organisation, such as a technology companies or the police. Prompted by the success of mobile games such as Pokémon Go, companies and governments have begun to 'crowdsource' their surveillance of public spaces. In this overview developed in cooperation with several European law enforcement agencies and members of civil society, we submit that it is still unclear whether Predictive Policing is effective if its main objective is to reduce crime rates. The main societal concerns relating to the use of Predictive Policing circle around the establishment of trust. The current legislative framework only partly addresses these issues, focusing mainly on individual rights and not on groups and how they might be affected. This translates into the legal domain and particularly questions relating to privacy. The main ethical issues relating to PP circle around the themes data selection and machine bias, visualisation and interpretation of forecasts, transparency and accountability, time and effectiveness as well as the problem of stigmatisation of individuals, environments and community areas. ![]() ![]() While Predictive Policing is an innovative tool to use data and statistical methods to forecast the probability of crime and improve the effectiveness of deployment of resources, it is based on many underpinning assumptions.
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