Trademark Search, Artificial Intelligence and the Role of the Private Sector

Almost every industry today confronts the potential role of artificial intelligence and machine learning in its future. While many studies examine AI in consumer marketing, less attention addresses AI's role in creating and selecting trademarks that are distinctive, recognizable, and meaningful to consumers. Traditional economic approaches to trademarks focus almost exclusively on consumer-based, demand-side considerations regarding search. However, these approaches are incomplete because they fail to account for substantial costs faced not just by consumers, but by trademark applicants as well. Given AI's rapidly increasing role in trademark search and similarity analysis, lawyers and scholars should understand its dramatic implications. This paper proposes that AI should interest anyone studying trademarks and their role in economic decision-making. We examine how machine learning techniques will transform the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. We run empirical experiments regarding trademark search to assess the efficacy of various trademark search engines, many of which employ machine learning methods. Through comparative analysis, we evaluate how these AI-powered tools function in practice. In an age where artificial intelligence increasingly governs trademark selection, the classic division between consumers and trademark owners deserves an updated, supply-side framework. This insight has transformative potential for encouraging both innovation and efficiency in trademark law and practice.

Private Accountability in the Age of Artificial Intelligence

In this Article, I explore the impending conflict between the protection of civil rights and artificial intelligence (AI). While both areas of law have amassed rich and well-developed areas of scholarly work and doctrinal support, a growing body of scholars are interrogating the intersection between them. This Article argues that the issues surrounding algorithmic accountability demonstrate a deeper, more structural tension within a new generation of disputes regarding law and technology. As I argue, the true promise of AI does not lie in the information we reveal to one another, but rather in the questions it raises about the interaction of technology, property, and civil rights. For this reason, I argue that we are looking in the wrong place if we look only to the state to address issues of algorithmic accountability. Instead, we must turn to other ways to ensure more transparency and accountability that stem from private industry, rather than public regulation. The issue of algorithmic bias represents a crucial new world of civil rights concerns, one that is distinct in nature from the ones that preceded it. Since we are in a world where the activities of private corporations, rather than the state, are raising concerns about privacy, due process, and discrimination, we must focus on the role of private corporations in addressing the issue. Towards this end, I discuss a variety of tools to help eliminate the opacity of AI, including codes of conduct, impact statements, and whistleblower protection, which I argue carries the potential to encourage greater endogeneity in civil rights enforcement. Ultimately, by examining the relationship between private industry and civil rights, we can perhaps develop a new generation of forms of accountability in the process.

Democracy and Distrust in an Era of Artificial Intelligence

This essay examines how judicial review should adapt to address challenges posed by artificial intelligence decision-making, particularly regarding minority rights and interests. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI-have combined to pose similar risks to minorities. Here, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability, offering a framework for judicial review in the AI era that protects minorities from algorithmic discrimination.