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人工智能系统中的偏见:整合正式和社会技术方法
Amar Ahmad1, Yvonne Vallès1, Youssef Idaghdour1
1Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Frontiers in big data
|January 26, 2026
概括
人工智能 (AI) 系统可以延续社会偏见. 本综述整合了技术和社会观点,以分类人工智能偏见,并提出了一个整个生命周期中公平人工智能发展的框架.
科学领域:
- 计算机科学 计算机科学
- 社会学 社会学 社会学
- 伦理学 伦理学 伦理学
背景情况:
- 人工智能 (AI) 系统在医疗保健和金融等关键领域普遍存在.
- 有证据表明,人工智能可以延续和加剧现有的社会不平等.
- 这引发了关于人工智能部署的重大伦理,社会和技术问题.
研究的目的:
- 在AI系统中正式定义和分类偏见.
- 检查人工智能偏见的起源,表现和影响.
- 批判性地评估当前的偏见缓解策略,并提出一个综合框架.
主要方法:
- 将偏见的正式数学定义与社会技术视角相结合.
- 偏见被分为四个类别:历史/代表性,选择/测量,算法/优化和反/出现.
- 跨领域的案例研究分析,包括面部识别,大型语言模型,信用评分,医疗保健,就业和刑事司法.
主要成果:
- 人工智能系统中的偏差来自多个相互关联的来源,并在各种应用中表现出来.
- 目前的缓解策略,包括数据集多样化和公平意识的建模,都有局限性.
- 将统计诊断与治理机制相结合的综合框架对于生命周期偏差管理至关重要.
结论:
- 解决人工智能偏见需要采用多学科的方法,将技术严谨与社会学理解相结合.
- 拟议的框架旨在指导公平,问责和响应性AI系统的发展.
- 这项研究为创造能够公平地为多样化的人口服务的AI提供了关键的见解.
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