Palgrave's Critical Policing Studies Predictive and The Construction of 'Criminal'

This book provides a cultural investigation of the police in India and how it uses data and algorithmic tools for crime mapping. It pays special attention to the discriminatory practices of relevant police officers and how this ‘predictive’ policing perpetuates harm to the most marginalised. This book provides a cultural investigation of the police in India and how it uses data and algorithmic tools for crime mapping.The book draws on an ethnographic study of Delhi Police's hotspot mapping endeavour. It provides a sociological investigation of the police in India and how they use data and algorithmic tools for crime mapping. It discusses how ‘criminals’ are constructed in these systems, typically, the marginalised residents of slums and immigrant colonies. It explores how the algorithm reifies existing assumptions and prejudices about 'criminals' as artificial intelligence systems are deeply intertwined with the culture and beliefs of those who make and use them. It pays special attention to the discriminatory practices of relevant police officers and how this ‘predictive’ policing perpetuates harm to the most marginalised. This book contributes to discussions around big data and surveillance studies broadly. Shivangi Narayan is an independent researcherbased in India and affiliated with the Algorithmic Governance and Cultures of Policing (AGOPOL) Project, funded by Oslo Metropolitan University, Norway and the Norwegian Research Council. Prior to being a researcher, she was a journalist with a fortnightly magazine, ‘Governance Now’, where she covered technology policy in India. She has a bachelor's degree in Electronics and Communication Engineering and an MA, M.Phil and PhD in Sociology. This book provides a cultural investigation of the police in India and how it uses data and algorithmic tools for crime mapping. The book draws on an ethnographic study of Delhi Police's hotspot mapping endeavour. It provides a sociological investigation of the police in India and how they use data and algorithmic tools for crime mapping. It discusses how ‘criminals’ are constructed in these systems, typically, the marginalised residents of slums and immigrant colonies. It explores how the algorithm reifies existing assumptions and prejudices about 'criminals' as artificial intelligence systems are deeply intertwined with the culture and beliefs of those who make and use them. It pays special attention to the discriminatory practices of relevant police officers and how this ‘predictive’ policing perpetuates harm to the most marginalised. This book contributes to discussions around big data and surveillance studies broadly.