Leveraging textual content, citational aspects and dissenting opinions through a multi-view contrastive learning methodology for legal precedent analysis

Artificial Intelligence is transforming the digital justice field by introducing technologies to automate document review, predict case outcomes, and perform legal research tasks. While offering significant benefits, these systems appear to prioritize decision-making patterns that are simply repeated over time, thus neglecting the importance of a dynamic evolution and potentially leading to the risk of stagnation of case law. To mitigate this risk, this paper proposes ContraLEX, a methodology based on a multi-view contrastive learning framework to compare legal judgments, considering those from the European Court of Human Rights as an emblematic case study. Methodologically, our goal is to capture the positive influence on the similarity, provided by both textual content and citations of precedents, and the negative influence of dissenting opinions, by relying on a contrastive learning approach. We argue that our methodology can enhance legal analysis by creating a proper representation of case law to prevent the stagnation of legal precedents and promote their evolution over time. A case study on ECtHR data empirically demonstrated that the proposed pipeline is very promising for properly supporting legal precedent analysis.