194 citations · 467 across the 14 of their papers we have counts for
3 papers · 1 filter
Fairness in Reinforcement Learning
Shahin Jabbari, Matthew Joseph, Michael Kearns +2
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requir…
Fair Algorithms for Infinite and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +2
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more ge…
Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +1
We introduce the study of fairness in multi-armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admissi…