ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

• URL: http://arxiv.org/abs/2105.13562v2
• Date: Mon, 31 May 2021 11:17:43 GMT
• Title: ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation
• Authors: Vijit Malik and Rishabh Sanjay and Shubham Kumar Nigam and Kripa Ghosh and Shouvik Kumar Guha and Arnab Bhattacharya and Ashutosh Modi
• Abstract summary: We propose the task of Court Judgment Prediction and Explanation (CJPE) CJPE requires an automated system to predict an explainable outcome of a case. Our best prediction model has an accuracy of 78% versus 94% for human legal experts.
• Score: 3.285073688021526
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanation (CJPE). The task requires an automated system to predict an explainable outcome of a case. We experiment with a battery of baseline models for case predictions and propose a hierarchical occlusion based model for explainability. Our best prediction model has an accuracy of 78% versus 94% for human legal experts, pointing towards the complexity of the prediction task. The analysis of explanations by the proposed algorithm reveals a significant difference in the point of view of the algorithm and legal experts for explaining the judgments, pointing towards scope for future research.

Related papers

• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Towards Explainability in Legal Outcome Prediction Models [64.00172507827499]
  We argue that precedent is a natural way of facilitating explainability for legal NLP models. By developing a taxonomy of legal precedent, we are able to compare human judges and neural models. We find that while the models learn to predict outcomes reasonably well, their use of precedent is unlike that of human judges.
  arXiv  Detail & Related papers  (2024-03-25T15:15:41Z)
• SLJP: Semantic Extraction based Legal Judgment Prediction [0.0]
  Legal Judgment Prediction (LJP) is a judicial assistance system that recommends the legal components such as applicable statues, prison term and penalty term. Most of the existing Indian models did not adequately concentrate on the semantics embedded in the fact description (FD) that impacts the decision. The proposed semantic extraction based LJP (SLJP) model provides the advantages of pretrained transformers for complex unstructured legal case document understanding.
  arXiv  Detail & Related papers  (2023-12-13T08:50:02Z)
• Fact-based Court Judgment Prediction [0.5439020425819]
  This extended abstract focuses on fact-based judgment prediction within the context of Indian legal documents. We introduce two distinct problem variations: one based solely on facts, and another combining facts with rulings from lower courts (RLC) Our research aims to enhance early-phase case outcome prediction, offering significant benefits to legal professionals and the general public.
  arXiv  Detail & Related papers  (2023-11-22T12:39:28Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction [46.71918729837462]
  Given the fact description text of a legal case, legal judgment prediction aims to predict the case's charge, law article and penalty term. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss. We propose a moco-based supervised contrastive learning to learn distinguishable representations. We further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model.
  arXiv  Detail & Related papers  (2022-11-15T15:53:56Z)
• Do Charge Prediction Models Learn Legal Theory? [59.74220430434435]
  We argue that trustworthy charge prediction models should take legal theories into consideration. We propose three principles for trustworthy models should follow in this task, which are sensitive, selective, and presumption of innocence. Our findings indicate that, while existing charge prediction models meet the selective principle on a benchmark dataset, most of them are still not sensitive enough and do not satisfy the presumption of innocence.
  arXiv  Detail & Related papers  (2022-10-31T07:32:12Z)
• Using attention methods to predict judicial outcomes [0.0]
  We have used AI classifiers to predict judicial outcomes in the Brazilian legal system. These texts formed a dataset of second-degree murder and active corruption cases. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets.
  arXiv  Detail & Related papers  (2022-07-18T16:24:34Z)
• Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction [28.505366852202794]
  Legal Judgment Prediction (LJP) aims to automatically predict judgment results, such as charges, relevant law articles, and the term of penalty. This paper concerns a worthwhile but not well-studied LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an appellate court on an appeal case. We propose a Sequential Multi-task Learning Framework with Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these challenges.
  arXiv  Detail & Related papers  (2022-03-09T08:51:13Z)
• Predicting Indian Supreme Court Judgments, Decisions, Or Appeals [0.403831199243454]
  We introduce our newly developed ML-enabled legal prediction model and its operational prototype, eLegPredict. eLegPredict is trained and tested over 3072 supreme court cases and has achieved 76% accuracy (F1-score) The eLegPredict is equipped with a mechanism to aid end users, where as soon as a document with new case description is dropped into a designated directory, the system quickly reads through its content and generates prediction.
  arXiv  Detail & Related papers  (2021-09-28T18:28:43Z)
• What About the Precedent: An Information-Theoretic Analysis of Common Law [64.49276556192073]
  In common law, the outcome of a new case is determined mostly by precedent cases, rather than existing statutes. We are the first to approach this question by comparing two longstanding jurisprudential views. We find that the precedent's arguments share 0.38 nats of information with the case's outcome, whereas precedent's facts only share 0.18 nats of information.
  arXiv  Detail & Related papers  (2021-04-25T11:20:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.