人文视角挑战生成式AI，揭示其认知局限与社会影响。
Provocations from the Humanities for Generative AI Research
- 从人文学科出发，提出8条关于生成式AI的核心批判性观点。
- 强调模型无法真正理解意义，且永远无法全面代表文化多样性。
- 适合关注AI伦理、文化批判与跨学科研究的学者与实践者。
生成式AI的影响遍及广泛群体，但其发展所依赖的学科视角却极为有限。本文提出人文学科研究者的一系列批判性观点，旨在丰富生成式AI未来的应用方向，并深化对其用途、影响与危害的讨论。基于相关人文学科文献及批判性数据研究基础，本文阐述了八个具有普遍适用性的主张：1）模型生成文字，但人类赋予意义；2）生成式AI需要更广义的文化定义；3）生成式AI永远无法实现代表性；4）模型越大并不意味着越好；5）并非所有训练数据都等同；6）开放性并非简单解决方案；7）计算资源受限助长企业垄断；8）人工智能普适性塑造狭隘的人类主体。文章还提供了人文学科研究的定义，总结其核心理论与方法，并将其应用于当前AI发展现状。最后，强调必须抵制计算机科学及相关领域对人文学科研究的单向汲取。
原文摘要 · Abstract (English)
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.
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