On the Fairness of 'Fake' Data in Legal AI

• URL: http://arxiv.org/abs/2009.04640v2
• Date: Fri, 11 Sep 2020 08:35:55 GMT
• Title: On the Fairness of 'Fake' Data in Legal AI
• Authors: Lauren Boswell, Arjun Prakash
• Abstract summary: We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.

Related papers

• Achieving Fairness in Predictive Process Analytics via Adversarial Learning [50.31323204077591]
  This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value.
  arXiv  Detail & Related papers  (2024-10-03T15:56:03Z)
• Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge [38.58529647679356]
  Legal Judgment Prediction aims to automatically predict a law case's judgment results based on the text description of its facts. The confusing law articles (or charges) problem frequently occurs, reflecting that the law cases applicable to similar articles (or charges) tend to be misjudged. This paper proposes an end-to-end model named textitD-LADAN to solve the above challenges.
  arXiv  Detail & Related papers  (2024-08-18T09:44:59Z)
• LawLLM: Law Large Language Model for the US Legal System [43.13850456765944]
  We introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain. LawLLM excels at Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP) We propose customized data preprocessing techniques for each task that transform raw legal data into a trainable format.
  arXiv  Detail & Related papers  (2024-07-27T21:51:30Z)
• 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)
• 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)
• Prototype-Based Interpretability for Legal Citation Prediction [16.660004925391842]
  We design the task with parallels to the thought-process of lawyers, i.e., with reference to both precedents and legislative provisions. After initial experimental results, we refine the target citation predictions with the feedback of legal experts. We introduce a prototype architecture to add interpretability, achieving strong performance while adhering to decision parameters used by lawyers.
  arXiv  Detail & Related papers  (2023-05-25T21:40:58Z)
• CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding [15.685369142294693]
  CaseEncoder is a legal document encoder that leverages fine-grained legal knowledge in both the data sampling and pre-training phases. CaseEncoder significantly outperforms both existing general pre-training models and legal-specific pre-training models in zero-shot legal case retrieval.
  arXiv  Detail & Related papers  (2023-05-09T12:40:19Z)
• 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)
• Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice [62.44110411199835]
  We build on literature in machine learning law to propose framework for limiting collection based on data interpretation that ties data to system performance. We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique.
  arXiv  Detail & Related papers  (2021-07-16T19:59:01Z)
• Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting [25.53133777558123]
  We introduce a novel dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction.
  arXiv  Detail & Related papers  (2021-07-12T04:27:14Z)

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.