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MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models
Addressing intersectional bias is crucial because it amplifies the negative effects of discrimination based on race, gender, and other identities.
Zero-Shot Ranking Socio-Political Texts with Transformer Language Models to Reduce Close Reading Time
We show that one can reduce the close reading time by taking some percentage of the ranked documents that the percentage depends on how much recall they want to achieve.
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