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20092024
most citedImproved Adversarial Learning for Fair Classification

28 citations · 44 across the 7 of their papers we have counts for

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Showing cs.CYShow all

6 papers · 1 filter

cs.CY2021

Auditing for Diversity using Representative Examples

Vijay Keswani, L. Elisa Celis

Assessing the diversity of a dataset of information associated with people is crucial before using such data for downstream applications. For a given dataset, this often involves c…

cs.CY2020

The Effect of the Rooney Rule on Implicit Bias in the Long Term

L. Elisa Celis, Chris Hays, Anay Mehrotra +1

A robust body of evidence demonstrates the adverse effects of implicit bias in various contexts--from hiring to health care. The Rooney Rule is an intervention developed to counter…

cs.CY2020

Dialect Diversity in Text Summarization on Twitter

Vijay Keswani, L. Elisa Celis

Discussions on Twitter involve participation from different communities with different dialects and it is often necessary to summarize a large number of posts into a representative…

cs.CY2020

Interventions for Ranking in the Presence of Implicit Bias

L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi

Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group (e.g., defined by gender or race). Studies on impl…

cs.CY2018

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…

cs.CY20175 cited

Fair Personalization

L. Elisa Celis, Nisheeth K. Vishnoi

Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to ea…