Probabilistic Obliteration and Formulaic Fabrication

What does it mean to cite generative AI (GenAI) tools—both in an instrumental, information retrieval sense, and in a symbolic sense that has more to do with recognition? What does it mean that GenAI tools are also able to produce plausible-looking yet false citations? This theoretical article critically explores the possibility of citational justice in the GenAI era through an analysis of two sets of examples: (1) the existing citation guidance to GenAI output as articulated by the major style guides, and (2) the issue of “hallucinated” (or fabricated) citations produced by GenAI large language model (LLM) chatbots like ChatGPT. Using ideas from Robert K. Merton, Eugene Garfield, Emily M. Bender, Robert J. Connors, and Sam Popowich, I argue that GenAI, across both sets of examples, is antithetical to citational justice. In the first set of examples, I make the case that the human authors and their works, the real source of GenAI tools’ textual output, have been “obliterated”—a term that I borrow from Merton—as part of LLM training. This renders the official style guides’ recommendations for how to cite GenAI tools—particularly APA’s guidance—deeply inadequate. In the second set of examples, I see the fabricated citations produced by GenAI as antithetical to citational justice because they decentre the human. Crucially, though, these fabricated citations are actually perfectly suited to the irrelational context of contemporary higher education transformed by neoliberalism, where commodified student outputs are made to stand as evidence of students’ internal transformation. As a closing gesture, I contend that the issues brought to the fore by GenAI and citation could present a pedagogical opportunity to radically reconceive of library instruction, focusing it on the importance of attribution and relationality in academic work if we so chose, and I offer some questions to guide that reimagined pedagogy.