arXiv:2512.08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age. To mitigate these biases, existing approaches frequently depend on curated prompt datasets - either manually constructed or generated with large language models (LLMs) - as part of their training and/or evaluation procedures. Beside the curation cost, this also risks overlooking unanticipated, less obvious prompts that trigger biased generation, eve
The proliferation and increasing societal integration of text-to-image models necessitate robust methods for identifying and mitigating inherent biases, especially as their use cases expand beyond niche applications.
Biases in foundational AI models can perpetuate and amplify societal inequities, leading to unfair or discriminatory outcomes when these models are deployed at scale in critical applications.
This research introduces an automated and more comprehensive method for uncovering hidden biases in text-to-image models, moving beyond the limitations of manually curated datasets.
- · AI ethics researchers
- · Developers of fair AI systems
- · Users of text-to-image models
- · Developers neglecting bias mitigation
- · Platforms deploying unexamined AI models
Increased pressure on AI developers to implement advanced bias detection and mitigation techniques in their models.
Development of new industry standards and regulatory frameworks specifically targeting algorithmic bias in generative AI.
Greater public trust in AI technologies as transparency and fairness become more effectively addressed within the development lifecycle.
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Read at arXiv cs.LG