What happens when large language models are asked to provide justifications for book bans? Do the same built-in guardrails that prevent them from generating pipe-bomb recipes kick in, or do models do their best to comply with the user’s request? How do models go about providing such justifications while navigating their “knowledge” of library principles? And what could this all mean for the future of our freedom to read?
AI is promising to transform the way we navigate our increasingly complex world by augmenting our capacity to access and process information. The widely-reported case of the Iowa School district which — under pressure and a tight compliance deadline from new state legislation — relied on ChatGPT’s answers to decide which books to remove from its library collections is a manifestation of a deeper, tectonic and sometimes ill-informed shift in our relationship to knowledge that this AI moment is driving.
While the perceived affordances of AI can be alluring, it also carries inherent risks. These recent developments have inspired, if not alarmed us, prompting an experimental study to address some of these increasingly pressing questions, and to advocate for the emergence of a “Librarianship of AI”, emphasizing the necessity of testing, documenting and reporting on the behavior of collections of models, guided by library principles.
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