Law Commons^™

Predicting Chapter 11 Bankruptcy Case Outcomes Using The Federal Judicial Center Idb And Ensemble Artificial Intelligence, Warren E. Agin, Gill Eapen

Georgia State University Law Review

In this project, the authors obtained public data on over 100,000 Chapter 11 bankruptcy cases and used machine and deep-learning methodologies to explore whether models could be designed to predict Chapter 11 case outcomes. The data used was obtained from the Federal Judicial Center’s bankruptcy Integrated Database and included information about case filing dates, the court where the case was filed, the type of business entity, and basic information about assets and liabilities. Using this information, the authors initially sought to predict whether a particular case was dismissed, converted to another Chapter under the Bankruptcy Code, or closed with a …

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 …

Legal Intelligence Through Artificial Intelligence Requires Emotional Intelligence: A New Competency Model For The 21st Century Legal Professional, Alyson Carrel

Georgia State University Law Review

The nature of legal services is drastically changing given the rise in the use of artificial intelligence and machine learning. Legal education and training models are beginning to recognize the need to incorporate skill building in data and technology platforms, but they have lost sight of a core competency for lawyers: problem-solving and decision-making skills to counsel clients on how best to meet their desired goals and needs. In 2014, Amani Smathers introduced the legal field to the concept of the T-shaped lawyer. The T-shaped lawyer stems from the concept of T-shaped professionals who have a depth of knowledge in …

Automation & Predictive Analytics In Patent Prosecution: Uspto Implication & Policy, Tabrez Y. Ebrahim

Georgia State University Law Review

Artificial-intelligence technological advancements bring automation and predictive analytics into patent prosecution. The information asymmetry between inventors and patent examiners is expanded by artificial intelligence, which transforms the inventor– examiner interaction to machine–human interactions. In response to automated patent drafting, automated office-action responses, “cloems” (computer-generated word permutations) for defensive patenting, and machine-learning guidance (based on constantly updated patent-prosecution big data), the United States Patent and Trademark Office (USPTO) should reevaluate patent-examination policy from economic, fairness, time, and transparency perspectives. By conceptualizing the inventor–examiner relationship as a “patenting market,” economic principles suggest stronger efficiencies if both inventors and the USPTO have better …

Where Do We Go From Here? Transformation And Acceleration Of Legal Analytics In Practice, Patrick Flanagan, Michelle H. Dewey

Georgia State University Law Review

The advantages of evidence-based decision-making in the practice and theory of law should be obvious: Don’t make arguments to judges that seldom persuade; Jurisprudential analysis ought to align with sound social science; Attorneys should pitch legal work to clients that demonstrably need it. Despite the appearance of simplicity, there are practical and attitudinal barriers to finding and incorporating data into the practice of law.

This article evaluates the current technologies and systems used to publish and analyze legal information from a researcher’s perspective. The authors also explore the technological, economic, political, and legal impediments that have prevented legal information systems …

Legal Analytics, Social Science, And Legal Fees: Reimagining "Legal Spend" Decisions In An Evolving Industry, Nancy B. Rapoport, Joseph R. Tiano Jr.

Georgia State University Law Review

To give you a feel for the power of legal analytics, imagine that you are the managing partner of a law firm. With a good set of algorithms and the push of a few buttons, you can make sure that you’ve delegated each part of an assignment to the professional with the exact combination of experience, talent, and diligence to maximize your firm’s client satisfaction and profitability. The client will be pleased both with the work product and its efficiency—and will pay your full bill without any grumbling or request for a reduction of the fees. The client will even …

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 …