How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
This addresses bias amplification in generative AI for image synthesis, which is an incremental but important step in understanding and mitigating dataset biases.
The paper investigates how sampling algorithms and hyperparameters in diffusion models influence bias amplification, showing that these factors can significantly reduce or amplify biases in models like Stable Diffusion, even with fixed trained models.
Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling algorithms and their hyperparameters influence bias amplification. We empirically demonstrate that samplers for diffusion models -- commonly optimized for sample quality and speed -- have a significant and measurable effect on bias amplification. Through controlled studies with models trained on Biased MNIST, Multi-Color MNIST and BFFHQ, and with Stable Diffusion, we show that sampling hyperparameters can induce both bias reduction and amplification, even when the trained model is fixed. Source code is available at https://github.com/How-I-met-your-bias/how_i_met_your_bias.