NSF Award
2204926
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. <br/><br/>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.<br/><br/>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.
