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Found 2 papers, 1 papers with code
Visual Identification of Problematic Bias in Large Label Spaces
With our approach, different models and datasets for large label spaces can be systematically and visually analyzed and compared to make informed fairness assessments tackling problematic bias.
Measuring Model Biases in the Absence of Ground Truth
By treating a classification model's predictions for a given image as a set of labels analogous to a bag of words, we rank the biases that a model has learned with respect to different identity labels.
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