Explainable Fairness Evaluation for Text-to-Image Diffusion Models in Underrepresented Cultural Contexts
Keywords:
explainable artificial intelligence, fairness evaluation, text-to-image diffusion models, cultural bias, underrepresented communities, sociotechnical systems, model governanceAbstract
Text-to-image diffusion models have achieved remarkable progress in generating high-fidelity visuals from natural language prompts, yet they frequently perpetuate and amplify cultural biases that marginalize underrepresented communities. Traditional fairness evaluation methods, which rely on aggregate statistical metrics, often fail to capture the nuanced ways in which cultural context interacts with model representations, and they provide little insight into why certain groups are disadvantaged. This paper proposes a comprehensive framework for explainable fairness evaluation specifically designed for text-to-image diffusion models operating in underrepresented cultural contexts. The framework integrates explainable artificial intelligence techniques, including feature attribution, concept-based explanations, and counterfactual reasoning, to produce interpretable diagnostics that link model outputs to specific cultural signals in training data and model internals. We examine the structural trade-offs inherent in designing such an evaluation infrastructure, including the tension between explanatory completeness and computational tractability, the challenge of grounding fairness metrics in culturally situated knowledge, and the governance implications of deploying these tools across global deployment pipelines. Through a detailed system-level analysis, we consider how the architecture of diffusion models, from latent space representations to cross-attention mechanisms, shapes the propagation of cultural biases. A case illustration drawn from recent empirical findings on cultural gaps in text-to-image generation [17] highlights the need for evaluation methods that move beyond superficial demographic parity toward a richer understanding of sociotechnical alignment. The paper further discusses policy implications, sustainability of fairness evaluation under data and resource constraints, and the role of community participation in defining fairness standards. We conclude by outlining future directions for building robust, transparent, and culturally aware fairness evaluation systems that can guide the responsible deployment of generative AI in diverse global settings.
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