FairJudge: Abstention-Aware Multimodal Judges for Fairness and Alignment Evaluation in Text-to-Image Models
arXiv:2510.22827v3 Announce Type: replace-cross Abstract: 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, indire
The proliferation of text-to-image models necessitates robust and nuanced evaluation methods, especially as concerns about AI ethics and bias become more prominent. This research directly addresses the current limitations in evaluating fairness and alignment.
Improving the evaluation of fairness and alignment in T2I models is crucial for responsible AI development, preventing societal harm, and building public trust in generative AI technologies.
The introduction of abstention-aware multimodal judges offers a more sophisticated and diagnostic approach to identifying biases and misrepresentations in generative AI outputs, moving beyond simplistic similarity metrics.
- · AI ethics researchers
- · Generative AI developers
- · Fairness evaluation platforms
- · Regulatory bodies
- · Developers ignoring ethical AI practices
- · Biased text-to-image models
Increased pressure on T2I model developers to integrate more sophisticated fairness evaluation tools.
Faster development of less biased and more context-aware generative AI models, leading to broader societal acceptance.
The development of industry-wide standards and benchmarks for equitable AI generation, potentially influencing regulatory frameworks.
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Read at arXiv cs.LG