arXiv:2605.28036v1 Announce Type: cross Abstract: Diffusion models steer conditional generation with a tunable guidance scale to trade off prompt alignment and diversity. However, existing debiasing techniques are optimized for a single scale, degrading fairness when users adjust this parameter. We trace this behavior to a previously overlooked source by decomposing total bias into two components: a model bias and a guidance bias. While prior work primarily targets the former, we show that the guidance bias grows monotonically with the guidance scale, eventually dominating the high-guidance re
The rapid deployment and increasing sophistication of diffusion models highlight the urgent need for robust fairness mechanisms that adapt to real-world usage parameters.
Ensuring fairness in AI models across varying operational conditions is critical for their ethical deployment and broad adoption, preventing systemic biases from being amplified by user interaction.
This research introduces a new understanding of bias decomposition in diffusion models, shifting focus from solely model bias to include guidance bias and offering pathways for more adaptive debiasing techniques.
- · AI ethicists
- · Developers of fair AI systems
- · Users of generative AI
- · Developers ignoring adaptive fairness
- · Systems with fixed debiasing strategies
Conditional generation from diffusion models becomes more inherently fair and less prone to bias amplification based on user guidance scales.
Increased trust and wider adoption of generative AI across sensitive applications where fairness is paramount.
This could accelerate regulatory frameworks and industry standards requiring dynamic fairness considerations in AI safety guidelines.
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Read at arXiv cs.LG