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Computational Statutory Reasoning

计算法定推理

基本信息

批准号：
2204926
负责人：
Benjamin Van Durme
金额：
$ 59.74万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204926&HistoricalAwards=false
关键词：
Computational Statutory Reasoning

项目摘要

Tax law is a huge, complex body of text, paralleling how the huge, complex U.S. economy is taxed. All three branches of government continually add new text: Congress adds to the Tax Code, the IRS issues interpretations, and courts write decisions in tax cases. It is challenging if not impossible for any single human to be aware of all of tax law. This can lead to entirely-sensible tax-law authorities interacting in ways unforeseen by their authors, enabling tax-avoidance strategies used by individuals and corporations with clever tax advisors. Such strategies cost the government billions of dollars and feed public perceptions of tax unfairness. Developing artificial intelligence (AI) that can automatically understand and reason with tax-law text would have two benefits. First, tax-avoidance strategies possible with existing tax law could be identified and shut down. Second, creators of new tax-law text (congressional staffers, IRS attorneys, and judges writing opinions in tax cases) could verify that they were not inadvertently enabling new tax-avoidance strategies. The aim of this project is to develop tools to automatically understand and reason with tax-law documents. This includes tax statutes and case law. The main research questions are how to reason about which statutes apply to a given case, how new statutes potentially impact previous decided cases, and how to automatically determine whether one case constitutes precedent for another case. First, this project will build benchmark datasets to measure progress on the above research goals, relying on existing expertise in dataset curation and on open legal data. Second, recent progress on converting textual data to structures supporting automated reasoning needs to be extended to the legal domain. This will require innovations in mapping language (statutes) into machine interpretable rules as compared to extracting text into data. Third, this project will develop legal domain ontologies, schemas, and information extraction models to analyze US case law. Progress on analyzing statutes and cases will involve extending capabilities in areas such as semantic parsing, entity typing, coreference, annotation science, schema induction and inference, AI system engineering, textual inference, and domain specialized language model pre-training. The effort will lead to new ways of thinking about the creation and use of legal language, with advances in natural language processing and automated reasoning, especially in the area of few-shot learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

税法是一个庞大而复杂的文本体系，与庞大而复杂的美国经济征税方式相似。政府的所有三个部门都在不断添加新文本：国会添加税法，国税局发布解释，法院撰写税务案件的裁决。这是具有挑战性的，如果不是不可能的任何一个人都知道所有的税法。这可能导致完全明智的税法当局以其作者无法预见的方式进行互动，使个人和公司能够通过聪明的税务顾问使用避税策略。这些策略花费了政府数十亿美元，并助长了公众对税收不公平的看法。开发能够自动理解税法文本并进行推理的人工智能（AI）有两个好处。首先，可以识别并关闭现有税法可能存在的避税策略。其次，新税法文本的制定者（国会工作人员、国税局律师和法官在税务案件中撰写意见）可以证明他们没有无意中促成新的避税策略。该项目的目的是开发工具，以自动理解和推理税法文件。这包括税法和判例法。主要的研究问题是如何推理哪些法规适用于给定的情况下，如何新的法规可能会影响以前决定的案件，以及如何自动确定一个案件是否构成另一个案件的先例。首先，该项目将建立基准数据集，以衡量上述研究目标的进展情况，依靠现有的数据集管理专业知识和开放的法律的数据。其次，最近在将文本数据转换为支持自动推理的结构方面取得的进展需要扩展到法律的领域。这将需要在将语言（法规）映射到机器可解释的规则方面进行创新，而不是将文本提取到数据中。第三，这个项目将开发法律的领域本体、模式和信息提取模型来分析美国判例法。分析法规和案例的进展将涉及扩展语义解析、实体类型、共指、注释科学、模式归纳和推理、人工智能系统工程、文本推理和领域专用语言模型预训练等领域的能力。这一努力将导致对创造和使用法律的语言的新的思维方式，在自然语言处理和自动推理的进步，特别是在少数镜头学习领域。这一奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。