Law Commons^™

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

Georgia State University Law Review

This paper surveys three basic legal-text analytic techniques—ML, network diagrams, and question answering (QA)—and illustrates how some currently available commercial applications employ or combine them. It then examines how well the text analytic techniques can answer legal questions given some inherent limitations in the technology. In more detail, ML refers to computer programs that use statistical means to induce or learn models from data with which they can classify a document or predict an outcome for a new case. Predictive coding techniques employed in e-discovery have already introduced ML from text into law firms. Network diagrams graph the relations between …

Artificial Intelligence And Law: An Overview, Harry Surden

Georgia State University Law Review

Much has been written recently about artificial intelligence (AI) and law. But what is AI, and what is its relation to the practice and administration of law? This article addresses those questions by providing a high-level overview of AI and its use within law. The discussion aims to be nuanced but also understandable to those without a technical background. To that end, I first discuss AI generally. I then turn to AI and how it is being used by lawyers in the practice of law, people and companies who are governed by the law, and government officials who administer the …

Ok, Google, Will Artificial Intelligence Replace Human Lawyering?, Melissa Love Koenig, Julie A. Oseid, Amy Vorenberg

Marquette Law Review

Will Artificial Intelligence (AI) replace human lawyering? The answer is

no. Despite worries that AI is getting so sophisticated that it could take over

the profession, there is little cause for concern. Indeed, the surge of AI in the

legal field has crystalized the real essence of effective lawyering. The lawyer’s

craft goes beyond what AI can do because we listen with empathy to clients’

stories, strategize to find the story that might not be obvious, thoughtfully use

our imagination and judgment to decide which story will appeal to an audience,

and creatively tell those winning stories.

This Article reviews …

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 …