This position paper argues that large language models (LLMs) can make cultural context, and therefore human meaning, legible at an unprecedented scale in AI-based sociotechnical systems. We argue that such systems have previously been unable to represent human meaning because they rely on thin descriptions (numerical representations that enforce standardization and therefore strip human activity of the cultural context which gives it meaning). By contrast, scholars in the humanities and qualitative social sciences have developed frameworks for representing meaning through thick description (verbal representations that accommodate heterogeneity and retain contextual information needed to represent human meaning). The verbal capabilities of LLMs now provide a means of at least partially automating the generation and processing of thick descriptions, offering new ways to deploy them at scale. We argue that the problem of rendering human meaning legible is not just about selecting better metrics but about developing new representational formats based on thick description. We frame this as a crucial direction for the application of generative AI and identify five key challenges: preserving context, maintaining interpretive pluralism, integrating perspectives based on lived experience and critical distance, distinguishing qualitative content from quantitative magnitude, and acknowledging meaning as dynamic rather than static.

翻译：本立场论文认为，在基于人工智能的社会技术系统中，大语言模型能够以前所未有的规模实现文化语境——进而实现人类意义——的可读化。我们指出，此类系统以往之所以无法表征人类意义，是因为其依赖"薄描述"（即强制标准化从而剥离人类活动文化语境——正是这种语境赋予活动以意义——的数值化表征）。相比之下，人文学科与定性社会科学学者已发展出通过"厚描述"（即容纳异质性并保留表征人类意义所需语境信息的言语化表征）来表征意义的理论框架。大语言模型的言语生成能力，如今至少为厚描述的生成与处理提供了部分自动化手段，从而为大规模应用厚描述开辟了新路径。我们认为，实现人类意义的可读化不仅关乎选择更优度量指标，更在于开发基于厚描述的新型表征范式。本文将这一方向定位为生成式人工智能应用的关键领域，并指出五大核心挑战：语境保持、阐释多元性维护、基于生活经验与批判距离的视角整合、定性内容与量化程度的区分，以及对意义动态性而非静态性的认知。