Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attributes are represented faithfully and without unsupported inference. Existing automated evaluators typically rely on face-centric recognizers or contrastive image–text similarity, which provide limited diagnostic feedback and often force predictions even when visual evidence is ambiguous or absent. For fairness-sensitive attributes such as religion and disability, where cues may be contextual, indirect, or intentionally unspecified, these evaluators can therefore miss failure modes that careful human reviewers would notice.

We introduce FairJudge, an abstention-aware evaluation protocol that uses instruction-following multimodal LLMs as structured judges for social-attribute prediction, profession grounding, and prompt–image alignment. The protocol constrains outputs to closed label sets, requires visible-evidence rationales, supports an explicit unspecified decision when cues are insufficient, and maps rubric-based alignment judgments to . These constraints turn MLLM judging from open-ended assessment into a parseable, auditable evaluation procedure.

Across four attribute-prediction benchmarks and three profession/alignment benchmarks, FairJudge outperforms or complements CLIP, DeepFace, VIEScore, and VQAScore. Ablations show that closed labels, abstention, and evidence reporting are central to reliability. We further introduce DIVERSIFY and DIVERSIFY-Professions, two context-rich resources for evaluating social representation and profession grounding beyond face-visible or iconic cues. We release code, prompts, datasets, parser logs, and per-image judge outputs to support reproducible auditing.

Multilingual vision–language models (VLMs) promise universal image–text retrieval, yet their social biases remain under‐explored.We perform the first systematic audit of four public multilingual CLIP variants—M‐CLIP, NLLB‐CLIP, CAPIVARA‐CLIP, and the debiased SigLIP‐2—covering ten languages that differ in resource availability and morphological gender marking.Using balanced subsets of FairFace and the PATA stereotype suite in a zero‐shot setting, we quantify race and gender bias and measure stereotype amplification.Contrary to the intuition that multilinguality mitigates bias, every model exhibits stronger gender skew than its English‐only baseline.CAPIVARA‐CLIP shows its largest biases precisely in the low‐resource languages it targets, while the shared encoder of NLLB‐CLIP and SigLIP‐2 transfers English gender stereotypes into gender‐neutral languages; loosely coupled encoders largely avoid this leakage.Although SigLIP‐2 reduces agency and communion skews, it inherits—and in caption‐sparse contexts (e.g., Xhosa) amplifies—the English anchor’s crime associations.Highly gendered languages consistently magnify all bias types, yet gender‐neutral languages remain vulnerable whenever cross‐lingual weight sharing imports foreign stereotypes.Aggregated metrics thus mask language‐specific “hot spots,” underscoring the need for fine‐grained, language‐aware bias evaluation in future multilingual VLM research.

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant ethical challenges, particularly in socially sensitive contexts. We introduce FairCoT, a novel framework that enhances fairness in text-to-image models through Chain-of-Thought (CoT) reasoning within multimodal generative large language models. FairCoT employs iterative CoT refinement to systematically mitigate biases, and dynamically adjusts textual prompts in real time, ensuring diverse and equitable representation in generated images. By integrating iterative reasoning processes, FairCoT addresses the limitations of zero-shot CoT in sensitive scenarios, balancing creativity with ethical responsibility. Experimental evaluations across popular text-to-image systems—including DALL-E and various Stable Diffusion variants—demonstrate that FairCoT significantly enhances fairness and diversity without sacrificing image quality or semantic fidelity. By combining robust reasoning, lightweight deployment, and extensibility to multiple models, FairCoT represents a promising step toward more socially responsible and transparent AI-driven content generation.