arXiv:2602.06806v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes. Prior work addresses this in two ways. Closed-set approaches mitigate biases in predefined fairness categories (e.g., gender, race), assuming socially salient minority attributes are known a priori. Open-set approaches frame the task as bias identification, highlighting majority attributes that dominate outputs. Both overlook a complementary task: uncovering rare or minority features
The rapid advancement and widespread deployment of text-to-image generative models are exposing their inherent biases, making the identification and mitigation of these issues a critical research area.
Biases in generative AI can perpetuate and amplify societal stereotypes, leading to unfair or inaccurate representations, which impacts ethical AI development and widespread adoption.
This work introduces a new method to identify previously overlooked 'rare' or 'minority' attributes in generative models, moving beyond known biases to uncover more subtle forms of discrimination.
- · AI ethics researchers
- · Companies building fair AI systems
- · Underrepresented communities
- · Developers ignoring bias mitigation
- · Generative AI models with unaddressed biases
Improved methods for auditing and debiasing text-to-image models become available to the research community.
Public demand and regulatory pressure for unbiased generative AI increase, leading to more robust ethical guidelines and best practices.
The development of truly inclusive and representative AI systems accelerates, fostering greater trust and broader societal benefit from AI technologies.
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