论文标题
在计算机视觉中升级：帕累托效率低下的深度分类器
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
论文作者
论文摘要
算法公平经常是在折衷的降低的权衡方面的动机，以提高算法算法不太准确的弱势群体的性能。与此相反，我们发现，将现有的公平方法应用于计算机视觉，通过降低所有组分类器的性能（在表现最佳的群体上降低）来改善公平性。 从理论上讲，我们将偏置变化的分解扩展到公平性，从理论上说明为什么为低容量模型设计的大多数公平分类器不应在涉及高容量模型的设置中使用，这是计算机视觉共有的场景。我们通过广泛的实验支持证实了这一分析，这表明计算机视觉中使用的许多公平启发式方法也降低了最弱势群体的性能。在这些见解的基础上，我们提出了一种自适应增强策略，在所有测试的方法中，它都可以提高弱势群体的绩效。
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness classifiers designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.
