Law Commons^™

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 …

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 …

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” …

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 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 …

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 …

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 …

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 …

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 …

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 …