把生成式AI当文化技术，用解读理论重新评估其意义。
Computational Hermeneutics: Evaluating generative AI as a cultural technology
- 将生成式AI视为需处理语境、多元与模糊性的'解读机器'
- 提出三原则：评估应迭代、包含人类、关注文化背景
- 适合关注AI文化影响与伦理的学者与设计者
生成式AI系统日益被视为文化技术，但现有评估框架常将文化当作可测量变量，而非系统运作的核心。基于人文学科的诠释学理论，我们指出生成式AI本质上是'情境机器'，必须应对三大解读挑战：情境性（意义仅在语境中产生）、多元性（多重有效解释共存）和模糊性（解释天然冲突）。我们提出计算诠释学作为新兴框架，提供对生成式AI如何运作及其改进路径的诠释性理解。该框架强调三项评价原则：评估应为迭代过程而非一次性测试；需纳入人类参与而非仅依赖机器；应衡量文化语境而非仅模型输出。这一视角为当代人工智能的设计与评估提供了新范式：从标准化的准确性问题转向关于意义的语境化问题。
原文摘要 · Abstract (English)
Generative AI systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the humanities, we argue that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness (meaning only emerges in context), plurality (multiple valid interpretations coexist), and ambiguity (interpretations naturally conflict). We present computational hermeneutics as an emerging framework offering an interpretive account of what GenAI systems do, and how they might do it better. We offer three principles for hermeneutic evaluation -- that benchmarks should be iterative, not one-off; include people, not just machines; and measure cultural context, not just model output. This perspective offers a nascent paradigm for designing and evaluating contemporary AI systems: shifting from standardized questions about accuracy to contextual ones about meaning.
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