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计算解释学:评估生成人工智能作为一种文化技术
Cody Kommers1, Ruth Ahnert2, Maria Antoniak3
1The Alan Turing Institute, London, United Kingdom.
Frontiers in artificial intelligence
|March 16, 2026
概括
生成型人工智能 (GenAI) 系统需要考虑文化背景的评估. 计算解释学为理解人工智能含义创造提供了一个新的框架,专注于上下文,而不仅仅是准确性.
科学领域:
- 人工智能的人工智能
- 人文科学 人文科学
- 计算语言学 计算语言学
背景情况:
- 生成性AI (GenAI) 系统越来越多地被视为文化技术.
- 当前的GenAI评估框架往往忽视了文化在系统运行中的基本作用.
- 现有的方法将文化视为可测量的变量,而不是内在的组成部分.
研究的目的:
- 提出计算解释学作为评估GenAI系统的框架.
- 通过结合从解释学理论的原则来重新构建GenAI评估.
- 将人工智能评估从准确度指标转变为上下文含义.
主要方法:
- 借鉴解释学理论来分析GenAI的功能.
- 确定GenAI的三个核心解释性挑战:定位性,多元性和模糊性.
- 开发人工智能系统的解释学评估原则.
主要成果:
- 基因人工智能系统作为"上下文机器"发挥作用,它们本质上与解释作斗争.
- 计算解释学提供了对GenAI操作的解释性解释.
- 建议的评估原则包括代基准,人类参与和上下文测量.
结论:
- 解释学方法对于理解和改进GenAI系统至关重要.
- 评估人工智能需要将重点从标准化的准确性转移到上下文含义.
- 这种观点为设计和评估当代人工智能提供了一个新的范式.
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