Artificial Intelligence in Forensic Psychological Risk Assessment: Predicting Criminal Behavior, Bias, and Ethical Boundaries

Abstract

The growing use of Artificial Intelligence (AI) in forensic psychology has reshaped the way criminal behavior and psychological risk factors are assessed. Conventional forensic evaluations largely depend on interviews, behavioral observation, and expert judgment, which may be influenced by subjectivity and human bias. AI-driven tools introduce a more systematic and data-oriented approach by examining behavioral patterns, psychological indicators, and past records to support risk assessment. This abstract examines the application of AI in forensic psychological risk assessment, with particular emphasis on predicting violent tendencies, likelihood of re-offending, and mental health vulnerabilities among offenders. A conceptual and review-based methodology has been adopted to analyze existing AI technologies, including machine learning models, predictive analytics, and behavioral assessment systems used within forensic and criminal justice settings. Alongside its advantages, the use of AI raises significant ethical challenges. Issues such as algorithmic bias, lack of transparency, data privacy concerns, and the potential misuse of automated predictions can have serious consequences in legal and mental health contexts. Over-dependence on AI outputs may lead to unfair profiling or compromised judicial decisions if not carefully regulated. The abstract highlights the importance of integrating AI tools with professional judgmentin forensic psychology. Establishing ethical frameworks, ensuring accountability, and maintaining interdisciplinary collaboration are essential for the responsible application of AI in criminal justice. This perspective is particularly relevant for forensic science students and professionals seeking to understand the balanced and ethical use of AI in psychological risk assessment.