Research on the Judicial Fairness Challenges and Legal Regulation of AI-Assisted Judgments in the Context of Digital Justice
TL;DR
This study investigates the fairness risks and regulatory mechanisms of AI-assisted judicial decision-making by establishing an integrated framework that combines algorithm interpretability, data governance, bias mitigation, and human–machine collaborative control.
Abstract
Artificial intelligence has become a critical enabling technology for intelligent decision-support systems, creating new opportunities and challenges for trustworthy information processing in digital governance environments. This study investigates the fairness risks and regulatory mechanisms of AI-assisted judicial decision-making by establishing an integrated framework that combines algorithm interpretability, data governance, bias mitigation, and human–machine collaborative control. The proposed framework systematically analyzes the impacts of black-box reasoning, training-data bias, and excessive technological dependence on decision reliability and procedural fairness. To address these challenges, explainable reasoning standards, full-lifecycle data governance strategies, algorithmic fairness auditing, and human-supervised decision protocols are incorporated into a unified regulatory architecture. Furthermore, a traceable reasoning mechanism and adaptive oversight framework are introduced to improve transparency, accountability, and operational robustness in AI-assisted decision systems. The proposed methodology provides practical guidance for explainable intelligent systems, trustworthy information processing, adaptive decision support, and distributed human–AI collaboration, offering potential references for intelligent sensing, secure information management, and next-generation digital service infrastructures.