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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

ISBN: 978-93-7183-003-4

DOI: https://doi.org/10.59646/704

Date of Publication: May 28, 2026

Cite this book: Xavier L, Surbhi G, Aswathi CN, Ravi K, and Nandini K, (2026), Computational Criminology: AI Applications in Forensic Science and Criminal Justice, Paradox International Publications & San International Scientific Publications, ISBN: 978-93-7183-003-4, DOI: https://doi.org/10.59646/704

Chapters and Authors

Chapter 1: Computational Criminology: Epistemological Foundations and the Algorithmic Turn in Criminal Justice Research – Mr. S. Karthick Raja

Chapter 2: From Crime Mapping to Machine Intelligence: A Historical Trajectory of Technology in Law Enforcement – Dr. K. Niranjana

Chapter 3: Data Ecosystems in Criminal Justice: Sources, Structures, and Ethical Architectures – Dr. Yogesh Kumar

Chapter 4: Theoretical Integration: Merging Criminological Theory with Computational Modelling – Shruthik Sharma

Chapter 5: Predictive Policing Algorithms: Mechanisms, Efficacy, and the Problem of Feedback Loops – Shivendra Pratap Singh

Chapter 6: Spatial Crime Analysis Using Geographic Information Systems and Deep Learning – Sejal Taran

Chapter 7: Temporal Crime Pattern Recognition: Sequence Modeling and Forecasting Recidivism Risk – Sree Durga Madhu

Chapter 8: Hot Spot Detection and Dynamic Risk Terrain Modeling Through Neural Networks – Jismol Thampi

Chapter 9: Gang Network Analysis and Violence Forecasting Using Graph-Based Machine Learning – Anshika Srivastava

Chapter 10: Digital Forensics and Artificial Intelligence: Automating Evidence Extraction and Analysis – Purva Jain

Chapter 11: Forensic DNA Phenotyping: Machine Learning Approaches to Biological Profiling – Labhini Lalit Rahangdale

Chapter 12: Computational Ballistics and Firearm Evidence Analysis Using Convolutional Neural Networks – Sneha Nair

Chapter 13: Handwriting and Signature Authentication: Deep Learning Approaches to Questioned Document Examination – Pallavi A Rao

Chapter 14: AI-Assisted Toxicology: Pattern Recognition in Postmortem Chemical Analysis – D. Padmashreee

Chapter 15: Bloodstain Pattern Analysis through Computer Vision and 3D Reconstruction – Satish Rai

Chapter 16: Facial Recognition Technology in Law Enforcement: Accuracy, Bias, and Civil Liberties – Rakesh Nair

Chapter 17: Voice and Speaker Identification: Advances in Forensic Phonetics and Neural Biometrics – Sherin Shaji

Chapter 18: Gait Analysis and Behavioural Biometrics as Emerging Tools in Suspect Identification – S Mahammad Asif

Chapter 19: Social Media Intelligence (SOCMINT) and Natural Language Processing in Criminal Investigations – Kiran Santhosh

Chapter 20: Deepfake Detection and the Integrity of Digital Evidence in the Courtroom – Reshma M Ashok

Chapter 21: Risk Assessment Instruments in Sentencing and Parole: Evaluating Actuarial Fairness – Mr. Naresh A

Chapter 22: Algorithmic Decision-Making in Pretrial Detention: Transparency, Accountability, and Due Process – Akash Chauhan

Chapter 23: AI Applications in Case Outcome Prediction and Legal Decision Support Systems – Saumya Tripathi

Chapter 24: Automated Threat Assessment in Correctional Facilities: Opportunities and Limitations – Jayshree

Chapter 25: Natural Language Processing in Legal Document Review, Case Summarisation, and Judicial Analytics – Anjali M S

Chapter 26: Algorithmic Bias and Racial Disparities in AI-Driven Criminal Justice Systems – Shreya Singh Parihar

Chapter 27: Explainability and Transparency in Black-Box Models: Toward Interpretable Justice – M. Oviya

Chapter 28: Privacy, Surveillance Creep, and the Fourth Amendment in the Age of AI Policing – Ms. Arya P J

Chapter 29: International Frameworks for the Governance of AI in Law Enforcement and Forensic Practice – Anjali.P.Nair

Chapter 30: The Future of Computational Criminology: Generative AI, Large Language Models, and the Next Decade of Criminal Justice Innovation – Keziya R Chitteth

Preface

The rapid advancement of artificial intelligence (AI), machine learning, big data analytics, and computational technologies has transformed numerous scientific disciplines, and criminology is no exception. In recent years, the emergence of Computational Criminology has revolutionized the way crime is studied, investigated, predicted, and prevented. By integrating traditional criminological theories with sophisticated computational methods, researchers and practitioners can now analyze vast datasets, uncover hidden crime patterns, forecast criminal activities, and support evidence-based decision-making within the criminal justice system. Simultaneously, forensic science has experienced significant technological innovation, with AI-driven tools enhancing the speed, accuracy, and reliability of evidence collection, analysis, and interpretation. These developments have created unprecedented opportunities for improving public safety, strengthening law enforcement capabilities, and advancing the pursuit of justice.

The book “Computational Criminology: AI Applications in Forensic Science and Criminal Justice” has been developed to provide a comprehensive exploration of this rapidly evolving interdisciplinary field. Bringing together contributions from scholars, researchers, and experts across criminology, forensic science, computer science, artificial intelligence, and legal studies, this volume presents a broad examination of how computational techniques are reshaping contemporary criminal justice systems. The chapters collectively highlight both the transformative potential of AI technologies and the ethical, legal, and social challenges associated with their adoption.

The initial chapters establish the theoretical and historical foundations of computational criminology. They explore the algorithmic transformation of criminal justice research, the evolution of technology in law enforcement, the growing importance of criminal justice data ecosystems, and the integration of criminological theories with computational modelling approaches. These foundational discussions provide readers with the conceptual framework necessary to understand the role of data-driven methods in analyzing crime and criminal behavior. The volume subsequently examines advanced applications such as predictive policing, crime forecasting, spatial crime analysis, hotspot detection, risk terrain modelling, and gang network analysis through machine learning and deep learning techniques. These approaches demonstrate how computational intelligence can assist law enforcement agencies in identifying emerging threats, allocating resources effectively, and developing proactive crime prevention strategies.

A major focus of this book is the application of AI in forensic science. Several chapters explore innovative technologies that are transforming forensic investigations, including digital forensics, forensic DNA phenotyping, computational ballistics, handwriting and signature verification, AI-assisted toxicology, bloodstain pattern analysis, facial recognition, voice identification, and behavioural biometrics. By leveraging computer vision, neural networks, pattern recognition systems, and advanced analytical algorithms, these technologies enhance forensic accuracy and support the objective evaluation of evidence. Such advancements are becoming increasingly important as criminal investigations generate growing volumes of digital and physical evidence that require rapid and reliable analysis.

Beyond investigative and forensic applications, the book also examines the expanding role of AI in judicial and correctional systems. Topics such as social media intelligence, deepfake detection, risk assessment instruments, algorithmic sentencing, pretrial decision-making, legal decision support systems, judicial analytics, and correctional threat assessment illustrate the increasing influence of computational tools throughout the criminal justice process. These technologies offer the potential to improve efficiency, consistency, and informed decision-making while simultaneously raising concerns regarding fairness, transparency, and accountability.

Recognizing these concerns, several chapters critically address issues of algorithmic bias, racial disparities, explainable AI, privacy protection, surveillance practices, and international governance frameworks for AI deployment in law enforcement and forensic science. Such discussions emphasize that technological innovation must be accompanied by ethical responsibility and robust regulatory oversight. The development of trustworthy and transparent AI systems remains essential to maintaining public confidence and protecting fundamental rights within democratic societies.

The concluding chapter explores future directions in computational criminology, including the emergence of generative AI, large language models, autonomous analytical systems, and next-generation forensic technologies. These developments are expected to further redefine criminal investigations, forensic practices, and justice administration in the coming decade.

This book is intended for researchers, students, criminologists, forensic scientists, law enforcement professionals, legal scholars, policymakers, and technology developers interested in the intersection of AI and criminal justice. It is hoped that this volume will serve as a valuable academic resource, stimulate interdisciplinary research, and contribute to the responsible development of intelligent technologies that enhance the effectiveness, fairness, and integrity of criminal justice systems worldwide.

 

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