Bridging the Black Box: Interpreting Transformer Attention in High-Stakes Medical and Legal AI

Transformer models have transformed many areas of research, showing exceptional performance in natural language processing and other areas. Their applications in high-stakes areas like healthcare and legal systems face a critical barrier: interpretability. As “black-box” systems, these models often lack the transparency required for stakeholders to understand their decision-making processes, which is crucial in scenarios where errors can have severe consequences. We introduce a novel framework that boosts the interpretability of transformer models through advanced attention visualization techniques. It is tailored to the needs of medical and legal domains, bridging the gap between raw model outputs and actionable insights. The approach incorporates domain-specific contextual layers, letting users pinpoint and understand key factors driving the model’s decisions. For example, attention maps produced by the framework can bring to the fore important regions in medical images or critical phrases in legal documents, thereby inducing trust and accountability. Overall evaluations using case studies in medical imaging with the CheXpert dataset and legal document analysis with the CaseLaw dataset illustrate the effectiveness of the proposed approach. The results show significant improvements in both performance and interpretability with high accuracy, precision, and recall achieved alongside clear visual explanations. This work advances the field of explainable AI by enabling the responsible and ethical integration of transformer models into critical applications, ensuring that these systems are not only high-performing but also transparent and trustworthy. This new framework will set a standard for the ethical and effective use of AI in high-risk domains.