Alarie & Cockfield on Machine-Authored Texts and the Future of Scholarship

Benjamin Alarie (University of Toronto – Faculty of Law) and Arthur J. Cockfield (Queen’s University – Faculty of Law) have posted “Will Machines Replace Us? Machine-Authored Texts and the Future of Scholarship” (Law, Technology and Humans, volume 3(2) (2021 Forthcoming) on SSRN. Here is the abstract:

We present here the first machine-generated law review article. Our self-interest motivates us to believe that knowledge workers who write complex articles drawing upon years of research and effort are safe from AI developments. However, how reasonable is it to persist in this belief given recent advances in AI research? With that topic in mind, we caused GPT-3, a state-of-the-art AI, to generate a paper that explains “why humans will always be better lawyers, drivers, CEOs, presidents, and law professors than artificial intelligence and robots can ever hope to be.” The resulting paper, with no edits apart from giving it a title and bolding the headings generated by GPT-3, is reproduced below. It is imperfect in a humorous way. Ironically, it is publishable “as-is” only because it is machine-generated. Nevertheless, the resulting paper is good enough to give us some pause for thought. Although GPT-3 is not up to the task of replacing law review authors currently, we are far less confident that GPT-5 or GPT- 100 might not be up to the task in the future.

Gutierrez on Trends in the Enforcement of Soft Law for the Governance of Artificial Intelligence

Carlos Ignacio Gutierrez (ASU Law) has posted “Transitioning from Ideas to Action: Trends in the Enforcement of Soft Law for the Governance of Artificial Intelligence” on SSRN. Here is the abstract:

As a governance tool, the advantages of soft law (e.g. lack of jurisdiction, minimal barriers of entry, and disposition for experimentation) make it a viable alternative to manage emerging technologies that are continuously evolving. A barrier to soft law’s utilization is its most cited weakness, a reliance on the alignment of incentives for its enforcement. Nevertheless, organizations throughout the globe have created mechanisms to ensure that the ideas within programs are transformed into action. This article explores the trends in the use of such mechanisms within soft law programs to govern methods and applications of artificial intelligence (AI). Using a database of over 600 AI soft law programs, this piece identifies the diverse array of options available to organizations in their efforts to implement and enforce their programs.