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Found 4 papers, 1 papers with code
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks.
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago.
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
An alternate approach that is commonly used is to separately train an attribute classifier on data with sensitive attribute information, and then use it later in the ML pipeline to evaluate the bias of a given classifier.
Crowdsourcing with Arbitrary Adversaries
Most existing works on crowdsourcing assume that the workers follow the Dawid-Skene model, or the one-coin model as its special case, where every worker makes mistakes independently of other workers and with the same error probability for every task.
