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Found 7 papers, 1 papers with code
BendVLM: Test-Time Debiasing of Vision-Language Embeddings
Vision-language model (VLM) embeddings have been shown to encode biases present in their training data, such as societal biases that prescribe negative characteristics to members of various racial and gender identities.
Identifying Implicit Social Biases in Vision-Language Models
Finally, we conduct an analysis of the source of such biases, by showing that the same harmful stereotypes are also present in a large image-text dataset used to train CLIP models for examples of biases that we find.
Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
Machine learning models can fail on subgroups that are underrepresented during training.
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation
Despite the growing popularity of methods which learn from unlabeled data, the extent to which these representations rely on spurious features for prediction is unclear.
Selective Classification Via Neural Network Training Dynamics
Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups.
