Law Commons^™

Can We Trust Artificial Intelligence In Criminal Law Enforcement?, Sara M. Smyth

Canadian Journal of Law and Technology

With the rapid advances made by AI in the last few years, yet with so much of it happening behind the scenes, it’s no wonder that most people are both baffled and awestruck by the capacity for these systems to render humans obsolete. Until recently, much of what the general public knew about AI, robotics, and their superhuman capabilities came from Hollywood blockbuster films like Minority Report. While it’s true that these films, in fact, provided a surprisingly realistic portrait of the capabilities that AI can now deliver, there is still a real lack of understanding on the part of …

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 …

Non-Autonomous Artificial Intelligence Programs And Products Liability: How New Ai Products Challenge Existing Liability Models And Pose New Financial Burdens, Greg Swanson

Seattle University Law Review

This Comment argues that the unique relationship between manufacturers, consumers, and their reinforcement learning AI systems challenges existing products liability law models. These traditional models inform how to identify and apportion liability between manufacturers and consumers while exposing litigants to low-dollar tort remedies with inherently high-dollar litigation costs.11 Rather than waiting for AI autonomy, the political and legal communities should be proactive and generate a liability model that recognizes how new AI programs have already redefined the relationship between manufacturer, consumer, and product while challenging the legal and financial burden of prospective consumer-plaintiffs and manufacturer-defendants.

Artificial Intelligence And Patent Ownership, W. Michael Schuster

Washington and Lee Law Review

Invention by artificial intelligence (AI) is the future of innovation. Unfortunately, as discovered through Freedom of Information Act requests, the U.S. patent regime has yet to determine how it will address patents for inventions created solely by AI (AI patents). This Article fills that void by presenting the first comprehensive analysis on the allocation of patent rights arising from invention by AI. To this end, this Article employs Coase Theorem and its corollaries to determine who should be allowed to secure these patents to maximize economic efficiency. The study concludes that letting firms using AI to create new technologies (as …

Artificial Intelligence Is Here, Get Ready!, Jessica G. Martz

Catholic University Journal of Law and Technology

No one is certain whether Artificial Intelligence (“AI”) will make the future a better place or make it look like an apocalyptic Hollywood blockbuster. An opinion that is emerging among experts and nation-state leaders is that the nation-states that lead in AI advancements and implementation will likely have a greater influence on and power over the world economic and national security stages. The goal of this book review is to encourage the reader to enter the conversation about the role AI will play in global society and American life because AI will influence the job market in the near future. …

The New Legal Landscape For Text Mining And Machine Learning, Matthew Sag

Faculty Articles

Now that the dust has settled on the Authors Guild cases, this Article takes stock of the legal context for TDM research in the United States. This reappraisal begins in Part I with an assessment of exactly what the Authors Guild cases did and did not establish with respect to the fair use status of text mining. Those cases held unambiguously that reproducing copyrighted works as one step in the process of knowledge discovery through text data mining was transformative, and thus ultimately a fair use of those works. Part I explains why those rulings followed inexorably from copyright's most …

Data-Informed Duties In Ai Development, Frank A. Pasquale

Faculty Scholarship

Law should help direct—and not merely constrain—the development of artificial intelligence (AI). One path to influence is the development of standards of care both supplemented and informed by rigorous regulatory guidance. Such standards are particularly important given the potential for inaccurate and inappropriate data to contaminate machine learning. Firms relying on faulty data can be required to compensate those harmed by that data use—and should be subject to punitive damages when such use is repeated or willful. Regulatory standards for data collection, analysis, use, and stewardship can inform and complement generalist judges. Such regulation will not only provide guidance to …

"Cyborg Justice" And The Risk Of Technological-Legal Lock-In, Rebecca Crootof

Law Faculty Publications

Although Artificial Intelligence (AI) is already of use to litigants and legal practitioners, we must be cautious and deliberate in incorporating AI into the common law judicial process. Human beings and machine systems process information and reach conclusions in fundamentally different ways, with AI being particularly ill-suited for the rule application and value balancing required of human judges. Nor will “cyborg justice”—hybrid human/AI judicial systems that attempt to marry the best of human and machine decisionmaking and minimize the drawbacks of both—be a panacea. While such systems would ideally maximize the strengths of human and machine intelligence, they might also …

Artificial Intelligence And Law: An Overview, Harry Surden

Publications

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 …

A Black Box For Patient Safety?, Nathan Cortez

Faculty Journal Articles and Book Chapters

Technology now makes it possible to record surgical procedures with striking granularity. And new methods of artificial intelligence (A.I.) and machine learning allow data from surgeries to be used to identify and predict errors. These technologies are now being deployed, on a research basis, in hospitals around the world, including in U.S. hospitals. This Article evaluates whether such recordings – and whether subsequent software analyses of such recordings – are discoverable and admissible in U.S. courts in medical malpractice actions. I then argue for reformulating traditional "information policy" to accommodate the use of these new technologies without losing sight of …

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 …

Lessons From Literal Crashes For Code, Margot Kaminski

Publications

No abstract provided.

Binary Governance: Lessons From The Gdpr’S Approach To Algorithmic Accountability, Margot E. Kaminski

Publications

Algorithms are now used to make significant decisions about individuals, from credit determinations to hiring and firing. But they are largely unregulated under U.S. law. A quickly growing literature has split on how to address algorithmic decision-making, with individual rights and accountability to nonexpert stakeholders and to the public at the crux of the debate. In this Article, I make the case for why both individual rights and public- and stakeholder-facing accountability are not just goods in and of themselves but crucial components of effective governance. Only individual rights can fully address dignitary and justificatory concerns behind calls for regulating …

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 …

The Right To Explanation, Explained, Margot E. Kaminski

Publications

Many have called for algorithmic accountability: laws governing decision-making by complex algorithms, or AI. The EU’s General Data Protection Regulation (GDPR) now establishes exactly this. The recent debate over the right to explanation (a right to information about individual decisions made by algorithms) has obscured the significant algorithmic accountability regime established by the GDPR. The GDPR’s provisions on algorithmic accountability, which include a right to explanation, have the potential to be broader, stronger, and deeper than the preceding requirements of the Data Protection Directive. This Essay clarifies, largely for a U.S. audience, what the GDPR actually requires, incorporating recently released …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

Recovering Tech's Humanity, Olivier Sylvain

Faculty Scholarship

No abstract provided.

Expanding Access To Remedies Through E-Court Initiatives, Amy J. Schmitz

Faculty Publications

Virtual courthouses, artificial intelligence (AI) for determining cases, and algorithmic analysis for all types of legal issues have captured the interest of judges, lawyers, educators, commentators, business leaders, and policymakers. Technology has become the “fourth party” in dispute resolution through the growing field of online dispute resolution (ODR), which includes the use of a broad spectrum of technologies in negotiation, mediation, arbitration, and other dispute resolution processes. Indeed, ODR shows great promise for expanding access to remedies, or justice. In the United States and abroad, however, ODR has mainly thrived within e-commerce companies like eBay and Alibaba, while most public …