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.