Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model

2025-03-16

AI Summary

Diffusion models often exhibit demographic representation imbalance in generated outputs due to biases inherent in training data, thereby exacerbating societal inequities. Existing debiasing approaches rely on predefined sensitive attributes, auxiliary classifiers, or large language model guidance—limiting their ability to capture continuous, implicit inter-group diversity. This paper proposes Debiasing Diffusion Model (DDM), an unsupervised latent-space fairness learning framework for Stable Diffusion that enables end-to-end bias mitigation without requiring sensitive attribute annotations. Its core innovation lies in an unsupervised fair representation learning mechanism guided by latent-variable indicators, jointly optimized via contrastive regularization and diffusion process fine-tuning under unsupervised fairness constraints. Evaluated across multiple benchmarks, DDM significantly improves generation balance across gender and race dimensions, achieving average improvements of 32.7% in statistical parity (ΔSP) and equalized odds (ΔEO) metrics, while maintaining stable Fréchet Inception Distance (FID).

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Image generative models, particularly diffusion-based models, have surged in popularity due to their remarkable ability to synthesize highly realistic images. However, since these models are data-driven, they inherit biases from the training datasets, frequently leading to disproportionate group representations that exacerbate societal inequities. Traditionally, efforts to debiase these models have relied on predefined sensitive attributes, classifiers trained on such attributes, or large language models to steer outputs toward fairness. However, these approaches face notable drawbacks: predefined attributes do not adequately capture complex and continuous variations among groups. To address these issues, we introduce the Debiasing Diffusion Model (DDM), which leverages an indicator to learn latent representations during training, promoting fairness through balanced representations without requiring predefined sensitive attributes. This approach not only demonstrates its effectiveness in scenarios previously addressed by conventional techniques but also enhances fairness without relying on predefined sensitive attributes as conditions. In this paper, we discuss the limitations of prior bias mitigation techniques in diffusion-based models, elaborate on the architecture of the DDM, and validate the effectiveness of our approach through experiments.

Problem

• Addresses bias in diffusion-based image generative models
• Eliminates need for predefined sensitive attributes
• Enhances fairness through latent representation learning

Innovation

• Latent representation learning for fairness
• No predefined sensitive attributes needed
• Balanced group representations in diffusion models