Forensic Linguistics and DBN
Abhinash Mohanta, Sneha, Kyvalya Garikapati, Sanskruti Mohanty, Shivani Samanta · Advances in computational intelligence and robotics book series · 2025
The integration of Deep Belief Networks (DBNs) into forensic linguistics enhances AI-driven policing in smart cities by automating witness testimony analysis. DBNs address limitations of traditional methods, such as subjectivity and inefficiency, by detecting linguistic markers of deception and inconsistency. AI tools have accelerated investigations by analyzing digital evidence, though specific architectures like DBNs were not disclosed in these cases. Ethical considerations, including algorithmic bias and data privacy, are addressed through strategies like adversarial debiasing and synthetic data augmentation. Collaboration between law enforcement, AI experts, and policymakers ensures ethical deployment and inclusive dataset curation. Future directions include expanding DBN applications to real-time threat detection and multilingual forensic analysis. DBNs improve urban security by enhancing testimony credibility assessments, fostering community trust, and aligning AI-driven policing with ethical standards.