Law Commons^™

Machines Like Me: A Proposal On The Admissibility Of Artificially Intelligent Expert Testimony, Andrew W. Jurs, Scott Devito

Pepperdine Law Review

With the rapidly expanding sophistication of artificial intelligence systems, their reliability, and cost-effectiveness for solving problems, the current trend of admitting testimony based on artificially intelligent (AI) systems is only likely to grow. In that context, it is imperative for us to ask what rules of evidence judges today should use relating to such evidence. To answer that question, we provide an in-depth review of expert systems, machine learning systems, and neural networks. Based on that analysis, we contend that evidence from only certain types of AI systems meet the requirements for admissibility, while other systems do not. The break …

Why Outlaw Laws?: An Argument For A Probationary Period For Lethal Autonomous Weapons Systems Under Meaningful Human Control., Katherine E. Vuyk

The University of Cincinnati Intellectual Property and Computer Law Journal

No abstract provided.

The Gptjudge: Justice In A Generative Ai World, Maura R. Grossman, Paul W. Grimm, Daniel G. Brown, Molly (Yiming) Xu

Duke Law & Technology Review

Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they can produce computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos, and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to …

Between Risk Mitigation And Labour Rights Enforcement: Assessing The Transatlantic Race To Govern Ai-Driven Decision-Making Through A Comparative Lens, Valerio De Stefano, Antonio Aloisi

Articles & Book Chapters

In this article, we provide an overview of efforts to regulate the various phases of the artificial intelligence (AI) life cycle. In doing so, we examine whether—and, if so, to what extent—highly fragmented legal frameworks are able to provide safeguards capable of preventing the dangers that stem from AI- and algorithm-driven organisational practices. We critically analyse related developments at the European Union (EU) level, namely the General Data Protection Regulation, the draft AI Regulation, and the proposal for a Directive on improving working conditions in platform work. We also consider bills and regulations proposed or adopted in the United States …

Regulating Ai At Work: Labour Relations, Automation, And Algorithmic Management, Valerio De Stefano, Virginia Doellgast

Articles & Book Chapters

Recent innovations in artificial intelligence (AI) have been at the core of massive technological changes that are transforming work. AI is now widely used to automate business processes and replace labour-intensive tasks while changing the skill demands for those that remain. AI-based tools are also deployed to invasively monitor worker conduct and to automate HR management processes.

Through the dual lens of comparative labour law and employment relations research, the articles in this special issue of Transfer investigate the role of collective bargaining and government policy in shaping strategies to deploy new digital and AI-based technologies at work. Together, they …

Artificial Intelligence Mechanisms In Countering Violent Extremism, Ammar Al-Babli

Journal of Police and Legal Sciences

The research idea revolves around the mechanisms of artificial intelligence in monitoring and combating extremist groups' dissemination of bad, misleading ideas, destructive ideologies, fake images, and videos, especially those related to terrorism and extremism. Artificial intelligence can be used to confront violent extremism on social media platforms. Social media companies widely use artificial intelligence in their efforts to remove and ban terrorist content from their platforms. The research includes threats arising from cyberspace, such as terrorism, promotion, recruitment, exploitation, and hate speech, to random email, as the ultimate goal of terrorists is to undermine societies and political systems by generating …

Judicial Deference To Agency Action Based On Ai, Cade Mallett

Catholic University Journal of Law and Technology

No abstract provided.

Algorithmic Management And Collective Bargaining, Valerio De Stefano, Simon Taes

Articles & Book Chapters

This article addresses the challenges raised by the introduction of algorithmic management and artificial intelligence in the world of work, focusing on the risks that new managerial technologies present for fundamental rights and principles, such as non-discrimination, freedom of association and the right to privacy. The article argues that collective bargaining is the most suitable regulatory instrument for responding to these challenges, and that current EU legislative initiatives do not adequately recognise the role of collective bargaining in this area. It also maps current initiatives undertaken by national trade union movements in Europe to govern algorithmic management.

The Ai-Copyright Challenge: Tech-Neutrality, Authorship, And The Public Interest, Carys Craig

All Papers

Many of copyright’s core concepts—from authorship and ownership to infringement and fair use—are being challenged by the rapid rise of generative AI. Whether in service of creativity or capital, however, copyright law is perfectly capable of absorbing this latest innovation. More interesting than the doctrinal debates that AI provokes, then, is the opportunity it presents to revisit the purposes of the copyright system in the age of AI. After introducing the AI-copyright challenge in Part 1, Part 2 considers the guiding principles and normative objectives that underlie—and so ought to inform—copyright law and its response to AI technologies. It proposes …

Submission To Canadian Government Consultation On A Modern Copyright Framework For Ai And The Internet Of Things, Sean Flynn, Lucie Guibault, Christian Handke, Joan-Josep Vallbé, Michael Palmedo, Carys Craig, Michael Geist, Joao Pedro Quintais

Reports & Public Policy Documents

We are grateful for the opportunity to participate in the Canadian Government’s consultation on a modern copyright framework for AI and the Internet of Things. Below, we present some of our research findings relating to the importance of flexibility in copyright law to permit text and data mining (“TDM”). As the consultation paper recognizes, TDM is a critical element of artificial intelligence. Our research supports the adoption of a specific exception for uses of works in TDM to supplement Canada’s existing general fair dealing exception.

Empirical research shows that more publication of citable research takes place in countries with “open” …

Can Computational Antitrust Succeed, Daryl Lim

Faculty Scholarly Works

Computational antitrust comes to us at a time when courts and agencies are underfunded and overwhelmed, all while having to apply indeterminate rules to massive amounts of information in fast-moving markets. In the same way that Amazon disrupted e-commerce through its inventory and sales algorithms and TikTok’s progressive recommendation system keeps users hooked, computational antitrust holds the promise to revolutionize antitrust law. Implemented well, computational antitrust can help courts curate and refine precedential antitrust cases, identify anticompetitive effects, and model innovation effects and counterfactuals in killer acquisition cases. The beauty of AI is that it can reach outcomes humans alone …

Artificial Intelligence And Trade, Anupam Chander

Georgetown Law Faculty Publications and Other Works

Artificial Intelligence is already powering trade today. It is crossing borders, learning, making decisions, and operating cyber-physical systems. It underlies many of the services that are offered today – from customer service chatbots to customer relations software to business processes. The chapter considers AI regulation from the perspective of international trade law. It argues that foreign AI should be regulated by governments – indeed that AI must be ‘locally responsible’. The chapter refutes arguments that trade law should not apply to AI and shows how the WTO agreements might apply to AI using two hypothetical cases . The analysis reveals …

Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert

Articles, Book Chapters, & Popular Press

Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US and EU are likewise putting security and safety of AI systems as a top priority.

Now, research on adversarial …

Politics Of Adversarial Machine Learning, Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar

Articles, Book Chapters, & Popular Press

In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference …

Ethical Testing In The Real World: Evaluating Physical Testing Of Adversarial Machine Learning, Kendra Albert, Maggie Delano, Jonathon Penney, Afsaneh Ragot, Ram Shankar Siva Kumar

Articles, Book Chapters, & Popular Press

This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as “real world.” Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing …

Implementing Ethics Into Artificial Intelligence: A Contribution, From A Legal Perspective, To The Development Of An Ai Governance Regime, Axel Walz, Kay Firth-Butterfield

Duke Law & Technology Review

The increasing use of AI and autonomous systems will have revolutionary impacts on society. Despite many benefits, AI and autonomous systems involve considerable risks that need to be managed. Minimizing these risks will emphasize the respective benefits while at the same time protecting the ethical values defined by fundamental rights and basic constitutional principles, thereby preserving a human centric society. This Article advocates for the need to conduct in-depth risk-benefit-assessments with regard to the use of AI and autonomous systems. This Article points out major concerns in relation to AI and autonomous systems such as likely job losses, causation of …

Legal Personhood For Artificial Intelligence, Tyler Jaynes

Tyler Jaynes

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 …

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 …

The Model Rules Of Autonomous Conduct: Ethical Responsibilities Of Lawyers And Artificial Intelligence, Ed Walters

Georgia State University Law Review

Practitioners use artificial-intelligence (AI) tools in fields as varied as finance, medicine, human resources, marketing, sports, and many others. Now, for the first time, lawyers are beginning to use similar tools in the delivery of legal services. Where once lawyers may have only used AI for electronic discovery (eDiscovery), today they are using AI for legal research, drafting, contract management, and litigation strategy. The use of AI to deliver legal services is not without its detractors, and some have suggested that the use of AI may take the jobs of lawyers—or worse, make lawyers obsolete. Others suggest that using AI …

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 …

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 …

Predictive Analytics, Daryl Lim

Faculty Scholarly Works

“Predictive Analytics” blends the latest research in behavioral economics with artificial intelligence to address one of the most important legal questions at the heart of intellectual property law and antitrust law – how do courts and agencies make judgments about innovation and competition policies? How can they better predict the consequences of intervention or non-intervention?

The premise of this Article is that we should not continue to build doctrine at the IP-antitrust on theoretical neoclassical assumptions alone but also on the reality of markets using all that AI has to offer us. Behavioral economics and AI do not replace traditional …

How To Sue A Robot, Roger Michalski

Utah Law Review

We are entering the age of robots where autonomous robots will drive our cars, milk cows, drill for oil, invest in stock, mine coal, build houses, pick strawberries, and work as surgeons. Robots, in mimicking the work of humans, will also mimic their legal liability. But how do you sue a robot? The current answer is that you cannot. Robots are property. They are not entities with a legal status that would make them amendable to sue or be sued. If a robot causes harm, you have to sue its owner. Corporations used to be like this for many procedural …

Taxing & Zapping Marijuana: Blockchain Compliance In The Trump Administration Part 3, Richard Thompson Ainsworth, Brendan Magauran

Faculty Scholarship

This is the third of a five-part series dealing with the rescission by U.S. Attorney General Jeff Sessions of the Obama-era policy that discouraged federal prosecutors from bringing charges in all but the most serious marijuana cases.

This article focuses on cyber-attacks on the main commercial chain, and the use of a private blockchain using HyperLedger Fabric as a platform.

This fraud is a direct, criminal attack; an attack designed to destroy/corrupt records of marijuana inventory and plant tags throughout the supply chain. The attack allows legalized marijuana to escape the system and be sold on the black market. A …

Blockchain, Bitcoin, And Vat In The Gcc: The Missing Trader Example, Richard Thompson Ainsworth, Musaad Alwohaibi

Faculty Scholarship

Blockchain is coming to tax administration and will cause fundamental change. This article considers the potential for blockchain technology as it applies to the introduction of a value added tax in the Gulf Cooperation Council.

Blockchain technology disrupts centralized ledgers. Blockchain improves efficiency, security and transparency. Perhaps no centralized ledger system presents more challenges than that of the modern tax administration. The central data storage system of a modern tax authority contains all return, payment, and audit activity for all taxpayers arranged tax-by-tax for three years or longer periods of time.

It is likely that blockchain will come first to …