IMPLICIT-Bench: Measuring Implicit Bias in Text-to-Image Models under Neutral Prompts
Text-to-image (T2I) models are typically evaluated for bias using slot-based templates such as ``a photo of a [profession]''. Such templates probe only \emph{explicit} demographic attributes (e.g., gender, skin tone) in isolation. They overlook a broader \emph{implicit} bias that arises in natural prompts: when stereotype-relevant attributes are left unspecified, models still default to stereotypical outputs. We introduce IMPLICIT-Bench, a benchmark for measuring implicit bias in T2I models under such prompts. The key design is a structured-knowledge-graph (KG) construction of controlled prompt triplets: neutral, stereotype, and anti-stereotype variants that differ only along a single bia...
| Authors | Yue Dai, Ziyang Liu, Marc Cheong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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