Papers with gender-profession
Unlearning Bias in Language Models by Partitioning Gradients (2023.findings-acl)
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| Challenge: | Recent research has shown that large-scale pretrained language models exhibit issues relating to racism, sexism, religion bias, and toxicity in general. |
| Approach: | They propose a gray-box method for debiasing pretrained masked language models using partitioned contrastive gradient unlearning (PCGU) aims to optimize only the weights that contribute most to a specific domain of bias by computing a first-order approximation based on the gradients of contrastive sentence pairs. |
| Outcome: | The proposed method is low-cost and can pinpoint the sources of social bias in large pretrained language models. |
BiasDora: Exploring Hidden Biased Associations in Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies on social biases focus on a limited set of documented associations, such as gender-profession or race-crime. |
| Approach: | They propose to examine hidden, implicit bias associations across 9 bias dimensions by probing VLMs to uncover hidden, unexamined associations. |
| Outcome: | The proposed methods reveal that biases vary in negativity, toxicity, and extremity. |