The Unfair Externalities of Exploration

By Manish Raghavan, Alex Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu. 2017

Citations in the corpus

• The Frontiers of Fairness in Machine Learning (2018, background): “[RSVW18] show that the costs associated with exploration can be un-fairly bourn by a structured sub-population, and that counter-intuitively, those costs can actually increase when they are included with a majority population, even though more data increases the rate of learning overall.”