减轻机器学习中与年龄相关的偏差的策略:范围审查
Charlene Chu1,2,3,4, Simon Donato-Woodger1, Shehroz S Khan2,5
1Lawrence Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
JMIR aging
|March 22, 2024
概括
机器学习 (ML) 模型中的数字年龄主义是一个问题. 本审查确定了减轻ML中年龄相关偏差的策略,重点关注数据平衡,增强和算法修改,以确保公平性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习伦理学 机器学习伦理学
背景情况:
- 数字年龄主义或与年龄相关的偏见在机器学习 (ML) 模型开发和部署中很普遍.
- 现有的研究突出了这个问题,但缺乏具体的策略和有效性分析,以减轻ML中与年龄相关的偏见.
研究的目的:
- 对旨在减少ML模型中与年龄相关的偏差的策略进行范围审查.
- 解决了解AI中数字时代主义缓解技术的差距.
主要方法:
- 遵守阿克西和奥马利的范围审查框架.
- 在6个主要的电子数据库和2个灰色文学数据库中进行全面的文献搜索.
- 与信息专家合作,改进搜索策略.
主要成果:
- 识别了8篇论文,解决了ML中与年龄相关的偏见.
- 偏见的主要原因被确定为数据集中老年人代表性不足.
- 缓解策略分为数据平衡,数据增强/补充和算法修改.
结论:
- 缓解ML中的偏见对于公平,公平和社会利益至关重要.
- 强调需要继续研究和开发有效的策略,以打击ML中的数字年龄主义.
- 强调确保ML系统服务于所有个人的利益.
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