Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models

AI can reveal hidden biases in judicial decisions: By analyzing thousands of French court decisions on alimony, the researchers used machine learning to identify whether judges were being influenced by factors outside the law (extra-legal factors). This approach helps detect potential bias rather than replacing human judgment.

Explainability matters more than accuracy alone: The study prioritizes interpretable AI models (random forest combined with classification and regression) over "black box" systems, making it possible to understand why specific factors influence alimony decisions. This transparency is essential for ensuring judicial fairness and public trust.

AI can support rather than replace legal professionals: Rather than automating court decisions, the researchers designed their model as a "complementary tool" to existing judicial guidelines and decision-making scales used by practitioners, positioning AI as an aid to human decision-makers rather than a substitute.

Data-driven insights can improve legal consistency: By identifying which variables legitimately determine alimony allocation versus which represent bias, courts could potentially develop better guidelines and training to ensure more consistent and legally-grounded decisions across different judges.

Large datasets enable bias detection in practice: The study demonstrates that analyzing substantial volumes of real court decisions (rather than hypothetical cases) can reveal patterns of extra-legal decision-making, providing concrete evidence to address systemic fairness issues in the judiciary.