SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP
Yusuke Hirota Min-Hung Chen Chien-Yi Wang Yuta Nakashima Yu-Chiang Frank Wang Ryo Hachiuma
Large-scale vision-language models, such as CLIP, are known to contain societal bias regarding protected attributes (e.g., gender, age). This paper aims to address the problems of societal bias in CLIP. Although previous studies have proposed to debias societal bias through adversarial learning or test-time projecting, our comprehensive study of these works identifies two critical limitations: 1) loss of attribute information when it is explicitly disclosed in the input and 2) use of the attribute annotations during debiasing process. To mitigate societal bias in CLIP and overcome these limitations simultaneously, we introduce a simple-yet-effective debiasing method called SANER (societal attribute neutralizer) that eliminates attribute information from CLIP text features only of attribute-neutral descriptions. Experimental results show that SANER, which does not require attribute annotations and preserves original information for attribute-specific descriptions, demonstrates superior debiasing ability than the existing methods.
Societal bias, CLIP, Debiasing, Fairness
We propose SANER, a debiasing method for CLIP that removes societal bias without requiring attribute annotations, while preserving attribute-specific information.