定向性和代表性是大型语言模型中刻板印象的可区分组成部分.
Gandalf Nicolas1, Aylin Caliskan2
1Department of Psychology, Rutgers University, New Brunswick, NJ 08873, USA.
PNAS nexus
|November 26, 2024
概括
这项研究介绍了代表性作为一种新方法来理解人工智能中的刻板印象,找到它.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
背景情况:
- 现有的人工智能偏见研究侧重于刻板印象的方向 (例如,能力与无能).
- 代表性,刻板印象的重点是特定的内容维度 (例如,温暖,能力),仍然没有得到充分的审查.
- 基于方向的偏差检测可能会错过AI模型中的代表性偏差.
研究的目的:
- 在AI模型中调查代表性作为刻板印象的独立属性.
- 为了检查代表性是否与刻板印象的方向不同,对于热情和能力的维度.
- 评估代表性和方向对人工智能模型价值和人类刻板印象的影响.
主要方法:
- 在使用大量社会类别样本的ChatGPT和SBERT语言模型中分析了刻板印象.
- 专注于温暖和能力的刻板印象维度.
- 检查了跨社会类别术语和种族化名称示例的代表性.
主要成果:
- 提供了证据,证明了温暖和能力刻板印象的方向和代表性的结构可区分性.
- 在不同的人工智能模型和目标刺激中展示了这些发现.
- 表明方向和代表性都独特地预测了AI价值和人类刻板印象.
结论:
- 代表性是人工智能刻板印象的一个独特和重要的特征,与方向分开.
- 人工智能偏见审计需要考虑刻板印象的方向和公平的代表性.
- 这些发现对认知科学和AI公平性研究中的AI有影响.
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