Artificial Intelligence in Witness Credibility Assessment: The Role of Biometrics, Voice Analytics and Machine Learning in Judicial Cross-Examination

Abstract

This study examines the integration of biometric recognition, voice analytics, and machine learning technologies in judicial cross-examinations to evaluate witness credibility. The methodology employed a mixed-methods approach combining biometric signal analysis (facial recognition, heart rate, skin conductance), voice stress analytics, and machine learning models (Support Vector Machines and Neural Networks) applied to courtroom data. Findings indicate that Neural Networks achieved the highest accuracy (97%) and recall (98%), outperforming traditional biometric and voice-based models, which showed moderate reliability. The study also reveals that while machine learning significantly improves predictive accuracy, challenges of transparency, bias, and ethical implications remain critical. These findings align with theories of technological mediation in legal decision-making, suggesting that AI can enhance but not replace human judgment in credibility assessment. The conclusion emphasises the necessity of explainable AI and ethical safeguards in legal technologies. Recommendations include the adoption of hybrid AI systems, continuous auditing for fairness, and maintaining judicial oversight to ensure justice is not compromised.