在整个工业生态应用中嵌入系统性公平:如何解决机器学习不公平和偏见的问题
Joe F Bozeman1,2, Catharina Hollauer1, Arjun Thangaraj Ramshankar1
1Civil & Environmental Engineering Georgia Institute of Technology Atlanta Georgia USA.
概括
这项研究解释了人工智能 (AI) 和机器学习 (ML) 应用程序在其整个生命周期中如何产生社会生态不平等. 它提供了指导,以解决AI/ML偏见和不平等在工业生态 (IE) 研究和实践.
科学领域:
- 工业生态学 工业生态学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 越来越多的需求将公平纳入AI/ML应用程序的研究和设计生命周期.
- 现有的框架缺乏针对工业生态学 (IE) 实践者实施公平AI/ML实践的具体指导.
- 在ML应用的不同阶段,社会生态不平等可能会出现.
研究的目的:
- 批判性地审查如何在整个生命周期中在ML应用中出现社会生态不平等.
- 举例来说明使用问卷来识别ML偏差 (数据,算法,选择/部署).
- 为解决AI/ML在IE中的不公平偏见和不平等提供指导和策略.
主要方法:
- 对ML生命周期阶段的批判性审查.
- 利用食品系统作为一个案例研究.
- 开发和示范一个全面的问卷,以确定ML偏见类别.
主要成果:
- 详细解释社会生态不平等如何在ML应用中表现.
- 展示一个问卷以界定数据偏差,算法偏差和选择/部署偏差.
- 综合指导和定制策略,以减轻AI/ML偏见和不公平.
结论:
- 人工智能/ML的不公平偏见和不平等影响了食品系统之外的各种IE领域,包括生活实验室和循环.
- 未来的IE方向应侧重于嵌入系统性公平,以了解和解决特定领域的不平等问题.
- 呼吁从根本上整合公平,以识别和减轻跨研究领域的ML潜在的不公平.
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