arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene only at the text encoder or provide inference-time guidance, often leading to generations that collapse into semantically incoherent outputs. To address these limitations, we introduce CO-ALIGN (Concept Ontology Alignment), a novel bias mitigation approach based on concept-graph alignment that operates on the model's internal concept ontology. By aligning concepts within the text encoder and denoise
The proliferation of Text-to-Image models necessitates robust bias mitigation strategies to prevent the embedding of harmful societal biases into AI-generated content, which is becoming increasingly ubiquitous.
Bias in AI models poses significant ethical, societal, and potentially legal challenges, and effective architectural solutions are crucial for the responsible development and deployment of AI systems at scale.
This research introduces a novel, architecturally integrated approach to bias mitigation in T2I models, moving beyond superficial fixes to address bias at a fundamental conceptual level within the model itself.
- · AI ethics researchers
- · Generative AI developers
- · Companies deploying T2I models
- · Users of AI-generated content
- · AI models with unmitigated bias
- · Platforms promoting biased content
Improved fairness and reduced propagation of harmful stereotypes in AI-generated images.
Increased trust and wider adoption of generative AI technologies in sensitive applications.
The establishment of new industry standards for bias mitigation techniques, influencing future AI model architectures.
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Read at arXiv cs.AI