FLAC:通过抑制属性-类协会来学习公平意识的代表性
IEEE transactions on pattern analysis and machine intelligence
|October 28, 2024
概括
本研究介绍了FLAC,这是一种用于训练公平计算机视觉模型的新方法,而不需要受保护的属性标签. 在各种数据集上,FLAC显著提高了公平性和准确性,超过了现有的技术.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能伦理学 人工智能伦理学
背景情况:
- 计算机视觉系统中的偏差可以使社会歧视延续.
- 现有的公平性方法通常需要受保护的属性标签,限制其实际使用.
- 标签不知情的公平性方法通常会产生较低的绩效.
研究的目的:
- 引入FLAC (Fairness through Learning Attribute Correlations),这是一个用于培训公平计算机视觉模型的新方法.
- 开发一种标签无意识的方法,尽量减少偏见,而不依赖于受保护的属性标签.
- 在各种数据集上提高计算机视觉模型的公平性和准确性.
主要方法:
- 在不使用属性标签的情况下,FLAC尽量减少模型特征和受保护属性之间的相互信息.
- 它采用抽样策略,强调代表性不足的数据样本.
- 该方法将公平性作为一种使用偏差捕获分类器的概率匹配问题.
主要成果:
- 在Biased-MNIST,CelebA和UTKFace上,FLAC实现了最先进的性能,远远超过现有方法 (29.1%,18.1%,21.9%).
- 这种方法在ImageNet-A (2.2%) 和Corrupted-Cifar10 (高达4.2%) 上显示出更好的准确性.
- 在实验评估中,FLAC通常甚至在偏见标签意识的最先进方法中表现优于FLAC.
结论:
- 即使没有明确的标签,FLAC也有效地学习独立于受保护属性的公平表示.
- 该方法为缓解计算机视觉偏差提供了一种实用且高性能的解决方案.
- FLAC代表了开发公平的人工智能系统的重大进步.
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