Artificial Intelligence techniques such as Machine Learning (ML) have not been exploited to their maximum potential in the legal domain. This has been partially due to the insufficient explanations they provided about their decisions. Automatic expert systems with explanatory capabilities can be specially useful when legal practitioners search jurisprudence to gather contextual knowledge for their cases. Therefore, we propose a hybrid system that applies ML for multi-label classification of judgements (sentences) and visual and natural language descriptions for explanation purposes, boosted by Natural Language Processing techniques and deep legal reasoning to identify the entities, such as the parties, involved. We are not aware of any prior work on automatic multi-label classification of legal judgements also providing natural language explanations to the end-users with comparable overall quality. Our solution achieves over 85 % micro precision on a labelled data set annotated by legal experts. This endorses its interest to relieve human experts from monotonous labour-intensive legal classification tasks.

Researchers at IIT Kharagpur have evolved an artificial intelligence-aided method to automate reading of legal judgements. A research team at the institute's Department of Computer Science and Engineering has developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgement, an IIT KGP statement said here. This could be unique in India where the country uses a Common Law system that prioritises the doctrine of legal precedence over statutory law and where legal documents are often written in an unstructured way, a member of the team said. "Taking 50 judgments from the Supreme Court of India, we have segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law," Saptarshi Ghosh, professor of the Department of Computer Science and Engineering, who is leading the research team, said. "We then performed extensive analysis of the human- assigned labels, and developed a high quality gold standard corpus to train the machine to carry out the task," Ghosh said.