一种偏见网络方法 (BNA) 鼓励人工智能开发人员进行道德反思
Gabriela Arriagada-Bruneau1,2, Claudia López3, Alexandra Davidoff4,5
1Instituto de Éticas Aplicadas, Instituto de Ingeniería Matemática y Computacional, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna, 4860, Santiago, Chile. gcarriagada@uc.cl.
Science and engineering ethics
|December 17, 2024
概括
偏见网络方法 (BNA) 帮助人工智能开发人员可视化和管理相互连接的偏见. 这种社会技术方法促进批判性思维和透明度,解决AI开发中孤立偏见管理的局限性.
科学领域:
- 人工智能的人工智能
- 社会技术系统 社会技术系统
- 人工智能伦理学
背景情况:
- 当前的AI偏见文献经常孤立地处理偏见,导致开发人员过载或不批判地接受.
- 这种"孤立主义方法"阻碍了有效的偏见管理和理解它们的相互联系.
- 需要一个社会技术的角度来解决人工智能偏见的人类和系统因素.
研究的目的:
- 介绍Bias网络方法 (BNA) 作为AI开发人员的社会技术方法.
- 为在整个AI开发生命周期中识别,绘制和关联偏见提供一个框架.
- 在开发团队中促进对人工智能偏见的对话和批判性反思.
主要方法:
- 开发了偏见网络方法 (BNA),这是一个社会技术框架.
- 利用图形表示来描绘偏见之间的相互联系.
- 在智利的医疗等待列表自然语言处理 (NLP) 模型上进行了试点案例研究.
- 涉及一个小型的人工智能开发团队和外部专家指导.
主要成果:
- BNA有效地可视化了相互关联的偏见及其影响,有助于道德反思.
- 实施BNA促进了人工智能开发决策的更大透明度.
- 该研究强调需要解决专业偏见和物质限制,因为它们是人工智能偏见的重要来源.
结论:
- 偏见网络方法 (BNA) 为管理AI偏见提供了一个有前途的社会技术方法.
- 可视化偏见的相互连接可以增强人工智能开发中的伦理考虑和透明度.
- 未来的研究应该关注人工智能开发过程中的专业偏见和物质限制.
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