46 citations · 165 across the 16 of their papers we have counts for
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cs.CV2023★ 7 cited
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
Junaid Ali, Matthaeus Kleindessner, Florian Wenzel +3
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then syst…
stat.ML2023★ 3 cited
Efficient fair PCA for fair representation learning
Matthäus Kleindessner, Michele Donini, Chris Russell +1
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic informa…