This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (AI) as interlocked policy areas. Predictive and AI technologies are growing in prominence and at an unprecedented rate. Powerful digital crime mapping programmes are being used to identify crime hotspots in real-time, as pattern-matching and search algorithms are sorting through huge police databases filled with growing volumes of data in an effort to identify high-risk people, intelligence and evidence. Facial and vehicle recognition cameras are locating criminals as they move, while police services develop strategies informed by machine learning programmes and other kinds of predictive analytics. Many of these innovations are features of modern policing in the U.K, the U.S and Australia, among other jurisdictions. AI, in particular, promises to reduce unnecessary labour, speed up various forms of police work, encourage police organisations to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade-offs to be made. Focusing on the U.K, the U.S. and Australia, this book explores themes of choice architecture, decision making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems and broader policing strategies. Written in a clear and direct style, this book will appeal to students and scholars of policing, criminology, crime science, sociology, computer science, cognitive psychology and all those interested in the emergence of AI as a feature of contemporary policing.