Provocations from the Humanities for Generative AI Research
It addresses the problem of integrating humanities insights into AI for researchers and practitioners, but it is incremental as it builds on existing critical data studies and humanities scholarship.
The paper tackles the need for humanities perspectives in generative AI research by presenting eight provocations that critique its uses, impact, and harms, such as claims about meaning-making and data representation, without providing concrete numerical results.
This paper presents a set of provocations for considering the uses, impact, and harms of generative AI from the perspective of humanities researchers. We provide a working definition of humanities research, summarize some of its most salient theories and methods, and apply these theories and methods to the current landscape of AI. Drawing from foundational work in critical data studies, along with relevant humanities scholarship, we elaborate eight claims with broad applicability to current conversations about generative AI: 1) Models make words, but people make meaning; 2) Generative AI requires an expanded definition of culture; 3) Generative AI can never be representative; 4) Bigger models are not always better models; 5) Not all training data is equivalent; 6) Openness is not an easy fix; 7) Limited access to compute enables corporate capture; and 8) AI universalism creates narrow human subjects. We conclude with a discussion of the importance of resisting the extraction of humanities research by computer science and related fields.