# Provocations from the Humanities for Generative AI Research

arXiv: https://arxiv.org/abs/2502.19190 · HTML: https://mlsanity.com/paper/2502.19190/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 175 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 37)

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Inverse Scaling: When Bigger Isn't Better](https://mlsanity.com/paper/2306.09479/) (2023)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://mlsanity.com/paper/2305.13169/) (2023)
- [Towards Measuring and Modeling "Culture" in LLMs: A Survey](https://mlsanity.com/paper/2403.15412/) (2024)
- [Investigating Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2402.13231/) (2024)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://mlsanity.com/paper/2309.12342/) (2023)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Careless Whisper: Speech-to-Text Hallucination Harms](https://mlsanity.com/paper/2402.08021/) (2024)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://mlsanity.com/paper/2105.05241/) (2021)
- [Confabulation: The Surprising Value of Large Language Model Hallucinations](https://mlsanity.com/paper/2406.04175/) (2024)
- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [Extrinsic Evaluation of Cultural Competence in Large Language Models](https://mlsanity.com/paper/2406.11565/) (2024)
- [Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets](https://mlsanity.com/paper/2406.18906/) (2024)
- [Algorithmic Pluralism: A Structural Approach To Equal Opportunity](https://mlsanity.com/paper/2305.08157/) (2023)
- [Does ChatGPT Have a Poetic Style?](https://mlsanity.com/paper/2410.15299/) (2024)
- [The Myth of Culturally Agnostic AI Models](https://mlsanity.com/paper/2211.15271/) (2022)
- [Vision-Language Models under Cultural and Inclusive Considerations](https://mlsanity.com/paper/2407.06177/) (2024)
- [Watching the Generative AI Hype Bubble Deflate](https://mlsanity.com/paper/2408.08778/) (2024)

## Built on by (most cited first, 10 of 10)

- [Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale](https://mlsanity.com/paper/2505.23785/) (2025)
- [Computational Hermeneutics: Evaluating generative AI as a cultural technology](https://mlsanity.com/paper/2604.16403/) (2026)
- [AI as Entertainment](https://mlsanity.com/paper/2601.08768/) (2026)
- [Estranged Predictions: Measuring Semantic Category Disruption with Masked Language Modelling](https://mlsanity.com/paper/2511.08109/) (2025)
- [The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisions](https://mlsanity.com/paper/2509.00248/) (2025)
- [Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation](https://mlsanity.com/paper/2608.05576/) (2026)
- [Calibrated Ambiguity in Multimodal Language Models: Humans reach for cultural references, while models describe the picture](https://mlsanity.com/paper/2609.12575/) (2026)
- [Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research](https://mlsanity.com/paper/2609.26023/) (2026)
- [Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn](https://mlsanity.com/paper/2610.12792/) (2026)
- [Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction](https://mlsanity.com/paper/2610.19028/) (2026)

## Abstract (arXiv)

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.