123 citations · 222 across the 8 of their papers we have counts for
12 papers
Reducing Access Disparities in Networks using Edge Augmentation
Ashkan Bashardoust, Sorelle A. Friedler, Carlos E. Scheidegger +2
In social networks, a node's position is a form of \it{social capital}. Better-positioned members not only benefit from (faster) access to diverse information, but innately have mo…
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
Mohsen Abbasi, Suresh Venkatasubramanian, Sorelle A. Friedler +2
Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over…
Problems with Shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger +1
Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models. These methods define a cooperative game between the features of…
Energy Usage Reports: Environmental awareness as part of algorithmic accountability
Kadan Lottick, Silvia Susai, Sorelle A. Friedler +1
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this pa…
Fair Meta-Learning: Learning How to Learn Fairly
Dylan Slack, Sorelle Friedler, Emile Givental
Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-lear…
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Dylan Slack, Sorelle Friedler, Emile Givental
Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The firs…