在人类与生成人工智能交互的实验模型中,刻板印象偏见的放大和逆转
Kevin Allan1, Jacobo Azcona1, Somayajulu Sripada1
1University of Aberdeen, Aberdeen, UK.
Royal Society open science
|April 10, 2025
概括
生成型人工智能 (AI) 放大了人类的性别刻板印象,但反刻板印象的AI建议可以扭转偏见. 保护措施对于减轻人工智能的影响至关重要.
科学领域:
- 心理学 心理学 心理学
- 人工智能伦理学 人工智能伦理学
- 人与计算机的交互
背景情况:
- 生成型人工智能很容易获得和表达刻板印象的偏见,引发社会对放大人类偏见的担忧.
- 之前的研究并没有经验证明人工智能扩大了与基线人类水平相比的刻板印象偏见.
- 了解AI对人类刻板印象的影响对于开发负责任的AI系统至关重要.
研究的目的:
- 调查生成性人工智能 (GPT3.5) 是否在人类第一印象中放大现有的性别刻板印象.
- 评估刻板印象与反刻板印象的人工智能生成角色描述对人类偏见的影响.
- 检查人工智能生成的解释在调制偏差放大和逆转中的作用.
主要方法:
- 四个实验 (N = 782) 使用了第一印象任务与人工智能生成的角色描述.
- 在由OpenAI的GPT3.5.5.生成的角色描述中控制的性别刻板印象.
- 对比"黑子"建议,详细阐述的刻板印象内容和反刻板印象内容的效果.
主要成果:
- 无法解释的,刻板印象的AI建议显著放大了第一印象中的基线人类刻板印象.
- 反刻板印象的建议消除和逆转了基线偏差,但影响力不如刻板印象的建议.
- 人工智能编制的内容显示了偏见放大和反转,解释增强了反刻板印象的影响.
结论:
- 如果没有保护措施,生成人工智能可能会放大现有的社会偏见.
- 人工智能保护措施可以有效地消除甚至逆转人类偏见.
- 新的方法可以安全地研究AI在不同环境中对社会刻板印象的影响.
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