KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation
arXiv:2606.01282v1 Announce Type: cross Abstract: Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of colour, older adults, and non-Western cultures as under-represented or caricatured) become a population-level harm at deployment scale. Existing mitigations either require costly retraining, infeasible for the closed-source backbones that dominate consumer products, or rely on fixed demographic templates that ignore c
The proliferation of text-to-image systems in public communication has brought their inherent biases to critical attention, necessitating immediate solutions to prevent population-level harm.
Bias in AI-generated imagery perpetuates harmful stereotypes at scale, impacting public perception, social equity, and the trustworthiness of AI in critical applications.
New methods for refining text-to-image prompts using knowledge graphs offer a way to mitigate demographic biases without costly retraining, making fair AI more accessible for existing deployed systems.
- · AI ethics researchers
- · companies deploying text-to-image systems
- · diverse user groups
- · journalism and public communication
- · developers relying on unmitigated biased models
- · closed-source AI model providers resistant to ethical enhancements
Wider adoption of bias mitigation techniques in generative AI systems becomes feasible.
Increased public trust in AI-generated content, potentially accelerating its integration into sensitive sectors.
The development of 'fairness-as-a-service' offerings for AI models, creating a new sub-industry.
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