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Provocations from the Humanities for Generative AI Research

Lauren Klein, Meredith Martin, André Brock, Maria Antoniak, Melanie Walsh, Jessica Marie Johnson, Lauren Tilton, David Mimno

TL;DR

The paper argues that generative AI development suffers from a narrow infusion of humanities perspectives, risking harms and misalignment with human needs. It introduces eight humanities-derived provocations rooted in language, culture, bias, data, openness, governance, and capitalism, each paired with actionable next steps to guide responsible research. By defining humanities inquiry and linking it to AI discourse, the work calls for equal interdisciplinary collaboration, broader methodological input, and resistance to extractive practices. The resulting framework aims to enrich AI with culturally informed, power-aware approaches that promote more humane and equitable technological futures.

Abstract

The effects of generative AI are experienced by a broad range of constituencies, but the disciplinary inputs to its development have been surprisingly narrow. Here we present a set of provocations from humanities researchers -- currently underrepresented in AI development -- intended to inform its future applications and enrich ongoing conversations about its uses, impact, and harms. Drawing from relevant humanities scholarship, along with foundational work in critical data studies, we elaborate eight claims with broad applicability to generative AI research: 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 also 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. We conclude with a discussion of the importance of resisting the extraction of humanities research by computer science and related fields.

Provocations from the Humanities for Generative AI Research

TL;DR

The paper argues that generative AI development suffers from a narrow infusion of humanities perspectives, risking harms and misalignment with human needs. It introduces eight humanities-derived provocations rooted in language, culture, bias, data, openness, governance, and capitalism, each paired with actionable next steps to guide responsible research. By defining humanities inquiry and linking it to AI discourse, the work calls for equal interdisciplinary collaboration, broader methodological input, and resistance to extractive practices. The resulting framework aims to enrich AI with culturally informed, power-aware approaches that promote more humane and equitable technological futures.

Abstract

The effects of generative AI are experienced by a broad range of constituencies, but the disciplinary inputs to its development have been surprisingly narrow. Here we present a set of provocations from humanities researchers -- currently underrepresented in AI development -- intended to inform its future applications and enrich ongoing conversations about its uses, impact, and harms. Drawing from relevant humanities scholarship, along with foundational work in critical data studies, we elaborate eight claims with broad applicability to generative AI research: 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 also 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. We conclude with a discussion of the importance of resisting the extraction of humanities research by computer science and related fields.

Paper Structure

This paper contains 23 sections, 1 table.