Papers by Jerone Andrews
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective (2024.emnlp-main)
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| Challenge: | Current methods to learn biases from the perspective of model components are limited by their complexity and performance. |
| Approach: | They propose a framework that incorporates causal mediation analysis to measure and map the pathways of bias generation and propagation within vision-language and multimodal tasks. |
| Outcome: | The proposed framework is applicable to a wide range of vision-language and multimodal tasks and reduces bias by 22.03% and 9.04% in the MSCOCO and PASCAL-SENTENCE datasets. |
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes (2024.emnlp-main)
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Yusuke Hirota, Jerone Andrews, Dora Zhao, Orestis Papakyriakopoulos, Apostolos Modas, Yuta Nakashima, Alice Xiang
| Challenge: | Traditional approaches only target labeled attributes, ignoring biases from unlabeled ones. |
| Approach: | They propose a method that ensures protected group independence from all attributes and mitigates inpainting biases through data filtering. |
| Outcome: | The proposed approach achieves an average reduction of 46.1% in leakage-based bias metrics for multi-label classification and 74.8% for image captioning. |