了解和减轻与对抗自动编码器的种族的影响
Kathryn Sarullo1, S Joshua Swamidass2
1Department of Computer Science, Washington University in St. Louis, St. Louis, MO, USA. ksarullo@wustl.edu.
Communications medicine
|October 22, 2024
概括
这项研究引入了一个对抗性的变异自编码器 (AVAE),用于在机器学习模型中创建种族独立的潜在空间. AVAE有效地删除种族信息,同时保持数据实用性,提供了一种新的方法来减轻人工智能的偏见.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 人工智能 (AI) 对社会挑战具有风险和机遇.
- 种族对机器学习模型的影响是一个关键的研究领域.
- 种族往往被隐含地编码在数据中,即使被排除为直接输入变量.
研究的目的:
- 开发一种方法来减轻种族对数据衍生模型的影响.
- 创建一个潜在的空间表示,不编码敏感的属性,如种族.
- 确保保留数据重建的基本信息.
主要方法:
- 使用了一个对抗变量自动编码器 (AVAE).
- 该模型被训练来学习一个低维的潜空间.
- 使用对抗程序来确保潜伏空间独立于种族.
主要成果:
- AVAE成功地从潜伏空间中删除了种族信息 (AUC ROC = 0.5).
- 其他方法未能有效地删除种族信息,允许高保真重建.
- 与传统模型 (R2 = 0.08) 不同的是,AVAE的潜空间对与种族相关的变量没有预测能力 (R2 = 0.003).
结论:
- 一个种族独立的潜伏空间被成功构建.
- AVAE方法可以概括用于控制其他敏感变量.
- 敌对变量自动编码器是管理和理解机器学习偏差的一个有希望的工具.
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