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This is a Experimental Study study.

September 20, 2025

CABIN: Debiasing Vision-Language Models Using Backdoor Adjustments

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CABIN demonstrates improved fairness in vision-language models by adjusting sensitive attributes in images, indicating effective bias mitigation.

CABIN effectively mitigates biases in vision-language models, enhancing fairness metrics across demographics.
Comprehensive experiments show that using CABIN maintains zero-shot inference capabilities while reducing bias.
The method involves a learned mapper to adjust sensitive attributes, ensuring independence from image embeddings.
Causal Adjustment Based INtervention provides a novel approach, avoiding the limitations of traditional debiasing methods.

Pang et al. (2025) studied this question.

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CABIN demonstrates improved fairness in vision-language models by adjusting sensitive attributes in images, indicating effective bias mitigation.