Authors - Md. Mehedi Rahman Rana, Md. Anisur Rahman, Kamrul Hasan Talukder, Syed Md. Galib Abstract - The adoption of AI in the law sphere on a larger scale has left new opportunities of case analysis and verdict prediction as well as legal texts interpretation with the help of the robot. However, the existing Legal Judgment Prediction (LJP) systems are submissible to implicit data bias, which contains adult information on such delicate aspects as gender, caste, occupation, and socio-economic status. These biases may result in ethically unsound and unreliable forecasting, which is a vital issue in high stakes judicial settings. This work provides a Bias-Aware Legal Case Classification and Judgment Interpretation architecture that enables improved levels of fairness, interpretability and contextual reliability in legal decision support systems. The bias-sensitive preprocessing pipeline proposed combines the Named Entity Recognition and zero-shot and legal-specific bias-tagging. These two types of vocabularies are used with a dual-encoder framework based on LegalBERT on bias-masked data and BERT on unmasked data in order to trade-off legal reasoning with controlled demographic awareness. Representations in a gating-based fusion mechanism are combined in advance to make final classification. The system is set to work on the real case documents of the Indian laws based on the publicly available repositories. Instead of substituting the jurisdictional powers, the framework is intended to deliver ethical, transparent, and contextually sensitive support to the legal practitioners. The research is relevant in the history of responsible AI, as it focuses on the issues of fairness and interpretability in the field of automated legal analytics.
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