Context Matters: Auditing Gender Bias in T2I Generation through Risk-Tiered Use-Case Profiles
Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting to be integrated into higher-impact pipelines. Since generated images tend to reinforce stereotypes, producing representational erasure via "default" depictions and shaping perceptions of who belongs in certain roles, a growing body of work has proposed metrics to quantify gender bias in T2I outputs. Yet existing evaluations remain fragmented. Metrics are often reported without a shared view of what they measure, what assumptions they entail, or how their results should be interpreted under different deployment contexts. This limits the use...
| Authors | Jose Luna, Yankun Wu, Xiaofei Xie et al. |
|---|---|
| Primary category | cs.CY |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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