28 citations · 44 across the 11 of their papers we have counts for
Showing 2019 · cs.LGShow all
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cs.LG2019
Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
Data containing human or social attributes may over- or under-represent groups with respect to salient social attributes such as gender or race, which can lead to biases in downstr…
cs.LG2019★ 28 cited
Improved Adversarial Learning for Fair Classification
L. Elisa Celis, Vijay Keswani
Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machi…
cs.LG2019
Implicit Diversity in Image Summarization
L. Elisa Celis, Vijay Keswani
Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which alre…