Recent advances in computer vision have led to the development of image classification models that can predict tens of thousands of object classes. Training these models can require millions of examples, leading to a demand of potentially billions of annotations. In practice, however, images are typically sparsely annotated, which can lead to problematic biases in the distribution of ground truth labels that are collected. This potential for annotation bias may then limit the utility of ground truth-dependent fairness metrics (e.g., Equalized Odds). To address this problem, in this work we introduce a new framing to the measurement of fairness and bias that does not rely on ground truth labels. Instead, we treat the model predictions for a given image as a set of labels, analogous to a 'bag of words' approach used in Natural Language Processing (NLP). This allows us to explore different association metrics between prediction sets in order to detect patterns of bias. We apply this approach to examine the relationship between identity labels, and all other labels in the dataset, using labels associated with 'male' and 'female') as a concrete example. We demonstrate how the statistical properties (especially normalization) of the different association metrics can lead to different sets of labels detected as having "gender bias". We conclude by demonstrating that pointwise mutual information normalized by joint probability (nPMI) is able to detect many labels with significant gender bias despite differences in the labels' marginal frequencies. Finally, we announce an open-sourced nPMI visualization tool using TensorBoard.
翻译:计算机视野的近期进步导致了图像分类模型的开发,可以预测成千上万个对象类别。 培训这些模型可能需要数百万个例子, 从而导致可能数十亿个注释的需求。 然而, 在实践中, 图像通常很少加注, 这可能在收集的地面真相标签的分布上造成问题性偏差。 这种批注偏差的可能性可能会限制地面依据真相的公平度量( 例如, 等, 均值奇数) 的效用。 为了解决这个问题, 我们在工作中引入了一个新的框架, 用于衡量不依赖地面真相标签的公平和偏差。 相反, 我们把对特定图像的模型预测作为一组标签, 类似于自然语言处理( NLP) 中使用的“ 一包单词” 方法。 这使我们能够探索不同指标集之间的不同关联性指标, 以检测身份标签和数据集中所有其他标签之间的关系, 使用与“ 男性” 和“ 女性” 相联的标签作为具体例子。 我们展示了统计性特性( ) 与不同指标类比的概率( ), 我们通过检测不同指标性标签最终检测“ ” 。