28 citations · 44 across the 7 of their papers we have counts for
9 papers · 1 filter
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes. Compared to prior work, our framework can be…
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…
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…
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…
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification…
An Algorithmic Framework to Control Bias in Bandit-based Personalization
L. Elisa Celis, Sayash Kapoor, Farnood Salehi +1
Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studi…