Provocations from the Humanities for Generative AI Research
🤖 AI Summary
This paper critically examines the techno-centric paradigm of generative AI from a humanities perspective, exposing deep-seated fallacies—including “scale worship,” the “illusion of data neutrality,” and “AI universalism”—that marginalize contextual, historical, and power-sensitive analysis. Method: Drawing on critical data studies, hermeneutics, cultural theory, and philosophy of technology, it advances eight foundational propositions (e.g., “models generate tokens; humans confer meaning”) to foreground situated interpretation, historicity, and power relations—rejecting purely engineering-oriented approaches. Contribution/Results: It establishes the first systematic humanities-based critical framework for generative AI, affirming the irreplaceable role of humanistic knowledge in AI governance. The work shifts the research focus from technical optimization toward ethical and epistemic reflection, while warning against computational resource monopolization and the unidirectional extraction and instrumentalization of humanistic knowledge.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Humanities perspective on generative AI
Impact and harms of generative AI
Resisting extraction of humanities research
Innovation
Methods, ideas, or system contributions that make the work stand out.
Humanities perspective on AI
Critical data studies application
Generative AI ethical claims
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