19 citations · 19 across the 2 of their papers we have counts for
5 papers
Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti, Alex Beutel, Jilin Chen +5
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fai…
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti +2
The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropri…
Operationalizing Individual Fairness with Pairwise Fair Representations
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of…
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairne…
Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
Preethi Lahoti, Kiran Garimella, Aristides Gionis
People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms t…