Computer Law Commons^™

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 …

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” …

Artificial Intelligence And The Challenges Of Workplace Discrimination And Privacy, Pauline Kim, Matthew T. Bodie

Scholarship@WashULaw

Employers are increasingly relying on artificially intelligent (AI) systems to recruit, select, and manage their workforces, raising fears that these systems may subject workers to discriminatory, invasive, or otherwise unfair treatment. This article reviews those concerns and provides an overview of how current laws may apply, focusing on two particular problems: discrimination on the basis of protected characteristics like race, sex, or disability, and the invasion of workers’ privacy engendered by workplace AI systems. It discusses the ways in which relying on AI to make personnel decisions can produce discriminatory outcomes and how current law might apply. It then explores …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Big Data And Artificial Intelligence: New Challenges For Workplace Equality, Pauline Kim

Scholarship@WashULaw

This essay contains remarks delivered in a keynote speech at the University of Louisville Brandeis School of Law’s 35th Annual Carl A. Warns and Edwin R. Render Labor and Employment Law Institute. Big data and artificial intelligence are increasingly being used by employers in their human resources processes in ways that control access to employment opportunities. This essay describes some of those developments and explains how practices like targeted online recruitment strategies and the use of hiring algorithms to screen applicants raise a significant risk of discriminating against protected groups such as women and racial minorities. It then considers some …

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 …