Download of the Week

The Download of the Week is “The Inevitable Conflict between Contract Law and Free Speech in Cyberspace” (Davies and Raczynska, Contents of Commercial Contracts: Terms Affecting Freedoms (Hart Publishing, 2020)) by Nicholas McBride. Here is the abstract:

This paper discusses why it is inevitable that contract law will be used as a means of censoring speech on the Internet, and why contract law allows itself to be used to limit freedom of speech.

Reinbold on Choosing Equality over Technology

Patric Reinbold (University of Wisconsin – Madison, School of Law) has posted “Facing Discrimination: Choosing Equality over Technology” to SSRN. Here is the abstract:

On its face, facial recognition technology poses advantages in the form of efficiency and cost-savings in sectors of society such as law enforcement, education, employment, and healthcare. However, these advantages perpetuate indirect forms of discrimination through unequal access to the technology’s benefits and—more significantly—direct forms of discrimination such as falsely identifying Black, Indigenous, and People of Color as suspects of crimes disproportionately. Facial recognition technology offers several opportunities to inject bias into its performance: through biased algorithm design, recycling racial bias in the form of past law enforcement data, and through biased user applications.

The precautionary principle warns against regulating a technology before it is fully developed and implemented, but the consequences of allowing this technology to go unregulated are overcome by the startling implications on racial discrimination in the United States. Therefore, this technology should be regulated before any further harm is done. This Comment analyzes the legislation proposed to regulate facial recognition technology by considering the longevity and breadth of the proposed regulations.

Lee & Schu on Algorithmic Trading Regulation

Joseph Lee (University of Exeter School of Law) and Lukas Schu have posted “Algorithmic Trading Regulation: The Frameworks for Human Supervision and Direct Market Interventions” to SSRN. Here is the abstract:

This paper examines the regulation of algorithmic trading in the capital markets and focuses on the human supervision and director market interventions. We compare the regulation in the UK, the EU and the US to find a common basis and additional regulatory techniques. In Part I, we examine the requirements for the internal risk management process a firm has to conduct before an algorithm can be implemented on the market in each of the three jurisdictions. In Part II, we examine the direct market intervention methods and focus on circuit breakers and assess their effectiveness. In Part III, we investigate the liability issue in the algorithmic trading space, including the liability of trading firms and venues for breaches of regulatory requirements, compensation claims of individual investors, and the liability of the regulators. The possible contributions of this paper are to fill the gap of human supervision and direct market interventions in algorithmic trading and build a novel framework for machine learning regulations in finance.

Marchant, Tournas & Gutierrez on Governing Emerging Technologies Through Soft Law

Gary E. Marchant (Arizona State University, Sandra Day O’Connor College of Law), Lucille Tournas (Arizona State University, Sandra Day O’Connor College of Law), and Carlos Ignacio Gutierrez (Arizona State University, Sandra Day O’Connor College of Law) have posted “Governing Emerging Technologies Through Soft Law: Lessons for Artificial Intelligence” (Jurimetrics, Vol. 61, Issue No. 1 (Fall 2020)) to SSRN. Here is the abstract:

Artificial Intelligence (AI) is positioned to be a foundational technology in most industrial sectors, societal interactions, as well as in many other technological advantages. AI is rapidly evolving with the promise of bettering our businesses, keeping us safer, and transforming us into a better society. At the same time, we know there will be concerns, some anticipated, and many that will develop alongside the technology itself. Its ubiquitous nature and rapid pace of development make traditional governance structures difficult to impose. However, there are a number of “soft-law” or non-legally binding tools that offer the flexibility needed to foster innovation safely.

Cihon, Maas & Kemp on Investigating Architectures for International AI Governance

Peter Cihon (Center for the Governance of AI, Future of Humanity Institute, University of Oxford), Matthijs M. Maas (Centre for the Study of Existential Risk, University of Cambridge, University of Cambridge – King’s College, Cambridge, University of Copenhagen – CECS- Centre for European and Comparative Legal Studies), and Luke Kemp (Australian National University (ANU) – The Fenner School of Environment and Society) have posted “Fragmentation and the Future: Investigating Architectures for International AI Governance” (Global Policy 11, no. 5 (November 2020)) to SSRN. Here is the abstract:

The international governance of artificial intelligence (AI) is at a crossroads: should it remain fragmented or be centralised? We draw on the history of environment, trade, and security regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak for centralisation. The risk of creating a slow and brittle institution, and the difficulty of pairing deep rules with adequate participation, speak against it. Other considerations depend on the specific design. A centralised body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial, and fragmented institutions could self-organise. In sum, these trade-offs should inform development of the AI governance architecture, which is only now emerging. We apply the trade-offs to the case of the potential development of high-level machine intelligence. We conclude with two recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, fragmentation will likely persist for now. The developing landscape should be monitored to see if it is self-organising or simply inadequate.

Katyal & Kasari on Trademark Search, Artificial Intelligence and the Role of the Private Sector

Sonia Katyal (University of California, Berkeley School of Law) and Aniket Kasari (University of California, Berkeley Data-Intensive Social Science Lab, Yale Law School) have posted “Trademark Search, Artificial Intelligence and the Role of the Private Sector” (Berkeley Technology Law Journal, Forthcoming) to SSRN. Here is the abstract:

Almost every industry today is confronting the potential role that artificial intelligence and machine learning can play in its future. While there are many, many studies on the role of AI in marketing to the consumer, there is less discussion of the role of AI in creation and selecting a trademark that is both distinctive, recognizable and meaningful to the average consumer. As we argue, given that the role of AI is rapidly increasing in trademark search and similarity areas, lawyers and scholars should be apprised of some of the dramatic implications that its role can produce.

We begin, mainly, by proposing, as a general matter, that AI should be of interest to anyone studying trademarks and the role that they play in economic decision-making. By running a series of empirical experiments regarding search, we show how comparative work can help us to assess the efficacy of various trademark search engines, many of which draw on a variety of machine learning methods. Traditional approaches to trademarks, spearheaded by economic approaches, have focused almost exclusively on consumer-based, demand side considerations regarding search. Yet as we show in this paper, these approaches are incomplete because they fail to take into account the substantial costs that are also faced by not just consumers, but trademark applicants as well.

In the end, as we show, machine learning techniques will have a transformative effect on the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. In an age where artificial intelligence will increasingly govern the process of trademark selection, we argue that the classic division between consumers and trademark owners is perhaps deserving of an updated, supply-side framework. As we argue, a new framework is needed, one that reflects that putative trademark owners, too, are ALSO consumers in the trademark selection ecosystem, and that this insight has transformative potential for encouraging both innovation and efficiency.