Book Title: Computational Criminology: AI Applications in Forensic Science and Criminal Justice
Editors: Dr. Xavier Louis, Dr. Surbhi Girdhar, Ms. Aswathi Chandran Nair, Mr. Ravi Kumar, and Ms. Nandini Katare
Chapter: 3
DOI: https://doi.org/10.59646/704/3
Author: Dr. Yogesh Kumar
Abstract
All algorithms for violence forecasts, risk assessments for recidivism, or mugshot-to-face recognition start with data. Seeking the analytical products of computational criminology, current and future researchers alike start with the raw materials of data. But if science history has taught us anything, it is that raw material is never really raw, being collected, chosen, sorted, cleaned, and stockpiled by institutional practices, political priorities, and social assumptions that are manifested by the very process of collection, but not in the resulting data set and therefore require careful attention.[1]
In this chapter, the data ecosystems which have supported the development of the field of computational criminology will be described: sources of criminal justice data, structures of organizations that might be used to store and process data, and ethical/legal architectures that shape data use. It is an argument that data quality, data architecture and data ethics are not merely technical aspects to be dealt with once the analytic model is drawn; rather, they are essential factors in whether the technology of computational criminology leads to democratically legitimate, just and accurate knowledge[2]. This chapter proceeds as follows. Section 3.2 maps the taxonomy of data sources available to criminal justice researchers and practitioners, examining their respective strengths and limitations. Section 3.3 analyses the principal dimensions of data quality and the ways in which quality failures propagate through analytical pipelines to produce flawed or discriminatory outputs. Section 3.4 describes the layered data architectures that characterise modern criminal justice information systems. Section 3.5 examines the ethical and legal frameworks that govern criminal justice data processing, with particular attention to the European General Data Protection Regulation and Law Enforcement Directive. Section 3.6 addresses the specific challenges of data ethics in computational criminology, including surveillance creep, discriminatory data, and rights of data subjects. The chapter concludes with reflections on the institutional conditions required for an ethically grounded criminal justice data ecosystem.[3]
[1]“Gary Marx, Windows into the Soul: Surveillance and Society in an Age of High Technology (University of Chicago Press, 2016) 3”.
[2]“Viktor Mayer-Schonberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think (Houghton Mifflin Harcourt, 2013) 6”.
[3]“Danielle Citron and Frank Pasquale, ‘The Scored Society: Due Process for Automated Predictions (2014) 89 Washington Law Review 1, 4”.