Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production

Abstract

Scholarly processes play a pivotal role in discovering, challenging, improving, advancing, synthesizing, codifying, and disseminating knowledge. Since the 17th Century, both the quality and quantity of knowledge that scholarship has produced has increased tremendously, granting academic research a pivotal role in ensuring material and social progress. Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices. Specifically, rather than the current “early-binding” process (that is, one in which ideas are fully reduced to a final written form before they leave an author’s desk), we propose that there are substantial benefits to a “late-binding” process, in which ideas are written dynamically at the moment of reading. In fact, the paradigm of “binding” knowledge may transition to a new model in which scholarship remains ever “unbound” and evolving. An alternative form for a scholarly work could be encapsulated via several key components: a text abstract of the work’s core arguments; hyperlinks to a bibliography of relevant related work; novel data that had been collected and metadata describing those data; algorithms or processes necessary for analyzing those data; a reference to a particular AI model that would serve as a “renderer” of the canonical version of the text; and specified parameters that would allow for a precise, word-for-word reconstruction of the canonical version. Such a form would enable both the rendering of the canonical version, and also the possibility of dynamic AI reimaginings of the text in light of future findings, scholarship unknown to the original authors, alternative theories, and precise tailoring to specific audiences (e.g., children, adults, professionals, amateurs). Among the myriad implications of this new paradigm of scholarship would be substantial challenges for copyright law, and, in particular, doctrines concerning authorship, ownership, derivative and transformative works, and compulsory licensing. We describe an iterative approach to scholarship that acknowledges the enduring value of previous form factors, including for historical, archival, and attribution purposes. Nevertheless, we propose that a streamlined, AI-supported scholarly process could enable more effective, timely, accessible, democratized, and evergreen scholarship.

Keywords: law, scholarship, artificial intelligence