28 citations · 44 across the 7 of their papers we have counts for
4 papers · 1 filter
Balanced News Using Constrained Bandit-based Personalization
Sayash Kapoor, Vijay Keswani, Nisheeth K. Vishnoi +1
We present a prototype for a news search engine that presents balanced viewpoints across liberal and conservative articles with the goal of de-polarizing content and allowing users…
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…
Fair and Diverse DPP-based Data Summarization
L. Elisa Celis, Vijay Keswani, Damian Straszak +3
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occu…