Bias Analysis in Unconditional Image Generative Models

TL;DR

This paper investigates bias in unconditional image generative models, revealing small attribute shifts influenced by classifier sensitivity and emphasizing the need for better evaluation practices to address social complexity.

Contribution

It provides an empirical analysis of bias shifts in generative models and highlights the impact of classifier decision boundaries on bias measurement.

Findings

Attribute shifts are generally small in unconditional models.

Classifier decision boundaries significantly influence bias detection.

Bias evaluation is sensitive to attribute spectrum and labeling practices.

Abstract

The widespread adoption of generative AI models has raised growing concerns about representational harm and potential discriminatory outcomes. Yet, despite growing literature on this topic, the mechanisms by which bias emerges - especially in unconditional generation - remain disentangled. We define the bias of an attribute as the difference between the probability of its presence in the observed distribution and its expected proportion in an ideal reference distribution. In our analysis, we train a set of unconditional image generative models and adopt a commonly used bias evaluation framework to study bias shift between training and generated distributions. Our experiments reveal that the detected attribute shifts are small. We find that the attribute shifts are sensitive to the attribute classifier used to label generated images in the evaluation framework, particularly when its decision boundaries fall in high-density regions. Our empirical analysis indicates that this classifier sensitivity is often observed in attributes values that lie on a spectrum, as opposed to exhibiting a binary nature. This highlights the need for more representative labeling practices, understanding the shortcomings through greater scrutiny of evaluation frameworks, and recognizing the socially complex nature of attributes when evaluating bias.