Measuring Model Biases in the Absence of Ground Truth

TL;DR

This paper introduces a mathematical approach to measure biases learned by models without relying on ground truth labels, using image classification as an example, and highlights the effectiveness of normalized PMI in bias ranking.

Contribution

The paper presents a novel method to quantify model biases through association metrics without needing annotated datasets, and provides an open-source visualization tool.

Findings

Normalized PMI (nPMI) effectively ranks biased labels.

Different association metrics yield varying bias rankings.

The method applies to multi-label classification without ground truth.

Abstract

The measurement of bias in machine learning often focuses on model performance across identity subgroups (such as man and woman) with respect to groundtruth labels. However, these methods do not directly measure the associations that a model may have learned, for example between labels and identity subgroups. Further, measuring a model's bias requires a fully annotated evaluation dataset which may not be easily available in practice. We present an elegant mathematical solution that tackles both issues simultaneously, using image classification as a working example. By treating a classification model's predictions for a given image as a set of labels analogous to a bag of words, we rank the biases that a model has learned with respect to different identity labels. We use (man, woman) as a concrete example of an identity label set (although this set need not be binary), and present rankings for the labels that are most biased towards one identity or the other. We demonstrate how the statistical properties of different association metrics can lead to different rankings of the most "gender biased" labels, and conclude that normalized pointwise mutual information (nPMI) is most useful in practice. Finally, we announce an open-sourced nPMI visualization tool using TensorBoard.