Psychological Implications of Artificial Intelligence in Judicial Decision-Making and Criminal Sentencing
DOI:
https://doi.org/10.47604/ijp.3659Keywords:
Artificial Intelligence, Judicial Decision-Making, Criminal Sentencing, Psychological Effects, Perceived Bias, Trust in Justice, Fairness PerceptionAbstract
Purpose: This study examines the psychological implications of integrating artificial intelligence (AI) into judicial decision-making in criminal justice, including algorithmically supported risk assessment and sentencing decisions. It analyzes how AI-based decision-support systems influence perceptions of fairness, trust in judicial decisions, and decision confidence, as well as the emotional responses of judges, jurors, defendants, and victims.
Methodology: The study employs a theory-driven and interdisciplinary conceptual framework grounded in psychological theories of decision-making, procedural justice, and affective processes. Through a critical integrative synthesis of legal, psychological, and ethical scholarship on algorithmic decision-making, predictive modeling, and risk assessment systems in criminal justice, the study examines their implications for human judgment, responsibility attribution, and judicial experience.
Findings: The analysis demonstrates that AI-assisted decision-making can substantially shape psychological perceptions of justice and the legitimacy of judicial processes. Although algorithmic tools are often perceived as consistent and objective, their reliance on historical data may reproduce existing biases, thereby negatively affecting perceived fairness, trust in judicial outcomes, and decision confidence among legal professionals and trial participants. These findings indicate that the psychological impact of artificial intelligence extends beyond technical accuracy and plays a significant role in shaping perceptions of the legitimacy of judicial processes.
Unique Contribution to Theory, Practice, and Policy: This study contributes to psychological theory by offering a systematic examination of the cognitive, affective, and evaluative processes associated with algorithmically supported judicial decision-making in criminal justice. In the context of judicial practice, the analysis demonstrates how uncritical reliance on AI systems may diminish judicial autonomy and obscure responsibility attribution in decision-making processes. From a public policy perspective, the findings contribute to the conceptualization of regulatory approaches oriented toward transparency, fairness, and trust in the use of AI in judicial decision-making.
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