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Law and Society

University of Pittsburgh School of Law

Artificial intelligence

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Full-Text Articles in Education

Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley Jan 2019

Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley

Articles

This paper examines impressive new applications of legal text analytics in automated contract review, litigation support, conceptual legal information retrieval, and legal question answering against the backdrop of some pressing technological constraints. First, artificial intelligence (Al) programs cannot read legal texts like lawyers can. Using statistical methods, Al can only extract some semantic information from legal texts. For example, it can use the extracted meanings to improve retrieval and ranking, but it cannot yet extract legal rules in logical form from statutory texts. Second, machine learning (ML) may yield answers, but it cannot explain its answers to legal questions or …


Using Ai To Analyze Patent Claim Indefiniteness, Dean Alderucci, Kevin D. Ashley Jan 2019

Using Ai To Analyze Patent Claim Indefiniteness, Dean Alderucci, Kevin D. Ashley

Articles

In this Article, we describe how to use artificial intelligence (AI) techniques to partially automate a type of legal analysis, determining whether a patent claim satisfies the definiteness requirement. Although fully automating such a high-level cognitive task is well beyond state-of-the-art AI, we show that AI can nevertheless assist the decision maker in making this determination. Specifically, the use of custom AI technology can aid the decision maker by (1) mining patent text to rapidly bring relevant information to the decision maker attention, and (2) suggesting simple inferences that can be drawn from that information.

We begin by summarizing the …


Computer Models For Legal Prediction, Kevin D. Ashley, Stephanie Bruninghaus Jan 2006

Computer Models For Legal Prediction, Kevin D. Ashley, Stephanie Bruninghaus

Articles

Computerized algorithms for predicting the outcomes of legal problems can extract and present information from particular databases of cases to guide the legal analysis of new problems. They can have practical value despite the limitations that make reliance on predictions risky for other real-world purposes such as estimating settlement values. An algorithm's ability to generate reasonable legal arguments also is important. In this article, computerized prediction algorithms are compared not only in terms of accuracy, but also in terms of their ability to explain predictions and to integrate predictions and arguments. Our approach, the Issue-Based Prediction algorithm, is a program …