Risks and Mitigation Strategies of Artificial Intelligence in Judicial Adjudication: A Human-AI Collaborative Framework for Judicial Reasoning

Artificial Intelligence (hereinafter referred to as AI) is being rapidly integrated into judicial adjudication, with big data-driven technologies playing a pivotal role. The transformation underpinning intelligent adjudication-a shift from text-based to code-driven judicial logic-has not only catalyzed the emergence of AI jurisprudence but also reshaped the concept of justice centered on the consistent adjudication of similar cases. However, the coexistence of complexity and opacity in AI-powered judicial systems may undermine their purported advantages. Beyond the black-box effects arising from the irreducibility of AI-driven judicial processes, issues persist in data and algorithms, two critical components of the AI judicial ecosystem. Consequently, judges' interpretive responsibilities now encompass new dimensions: verifying and integrating the foundational conditions of AI-assisted adjudication. To align AI technologies with the objectives of human judicial decision-making, judges should focus on four hermeneutic checkpoints through human-machine collaboration: (1) Is the case dataset sufficiently comprehensive? (2) Is the adjudicative outcome normatively justified? (3) Is the causal relationship appropriately established? (4) Is the symmetry between affirmative and negative computational reasoning maintained.