AI-DRIVEN FORENSIC EVIDENCE VALIDATION EMPIRICAL FRAMEWORK AND ETHICAL DILEMMAS IN PREDICTIVE POLICING: REDEFINING LEGAL ACCOUNTABILITY IN ALGORITHMIC JUSTICE SYSTEMS FOR REGULATING MACHINE BIAS, TRANSPARENCY, AND DUE PROCESS IN NEXT-GEN CRIMINAL INV

As artificial intelligence (AI) becomes deeply embedded in criminal justice systems, particularly through predictive policing and forensic evidence evaluation, ethical, legal, and technical challenges are intensifying. This paper examines a 2025-contextualized empirical framework for AI-driven forensic validation while addressing pressing ethical dilemmas in predictive policing systems. We explore how machine bias, lack of transparency, and due process violations can compromise justice. A reform-centered framework is proposed to ensure legal accountability in algorithmic decision-making, emphasizing auditability, explainability, and compliance with democratic principles. The discussion is supported by literature and graphical models showing interdependencies between ethics, law, and computational technologies in next-gen investigations.