activity
20172020
most citedWhat's Sex Got To Do With Fair Machine Learning?

69 citations · 97 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG202015 cited

Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics

John Burge, Matthew Bonanni, Matthias Ihme +1

As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, respo…

cs.CY202069 cited

What's Sex Got To Do With Fair Machine Learning?

Lily Hu, Issa Kohler-Hausmann

Debate about fairness in machine learning has largely centered around competing definitions of what fairness or nondiscrimination between groups requires. However, little attention…

cs.LG20194 cited

Fair Classification and Social Welfare

Lily Hu, Yiling Chen

Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this…

cs.IT2018

Secure Relaying in Non-Orthogonal Multiple Access: Trusted and Untrusted Scenarios

Ahmed Arafa, Wonjae Shin, Mojtaba Vaezi +1

A downlink single-input single-output non-orthogonal multiple access setting is considered, in which a base station (BS) is communicating with two legitimate users in two possible…

cs.LG2018

Welfare and Distributional Impacts of Fair Classification

Lily Hu, Yiling Chen

Current methodologies in machine learning analyze the effects of various statistical parity notions of fairness primarily in light of their impacts on predictive accuracy and vendo…

cs.SI20171 cited

Activating the "Breakfast Club": Modeling Influence Spread in Natural-World Social Networks

Lily Hu, Bryan Wilder, Amulya Yadav +2

While reigning models of diffusion have privileged the structure of a given social network as the key to informational exchange, real human interactions do not appear to take place…