A Multi-Paradigm Interpretability Framework for Case Outcome Prediction with Causal Reasoning
This conference paper proposes a multi-paradigm interpretability framework for legal case outcome prediction, emphasizing not only predictive accuracy but also explanation validity and causal reasoning. The work combines statistical pattern recognition, knowledge graph modeling, attention mechanisms, and causal inference to explore explainable AI for legal decision-support scenarios.
Presented as a poster at the 2026 4th IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI 2026). Authors visible on the poster include Ning Mou, Shuo Han, Yi-Chi Zhang, and Fan Wu.
面向案件结果预测的多范式可解释性框架：结合因果推理
本研究提出一种面向法律案件结果预测的多范式可解释性框架，不仅关注预测准确率，也强调解释有效性与因果合理性。该框架结合统计模式识别、知识图谱、注意力机制与因果推理，用于探索法律判断辅助场景中可解释 AI 模型的实际应用价值。
该论文在2026年第四届IEEE模式识别、机器视觉与人工智能国际会议（PRMVAI 2026）上以海报形式发表。海报列出的作者包括 Ning Mou、Shuo Han、Yi-Chi Zhang、Fan Wu。