Understand
We study fairness within the stochastic, \emph{multi-armed bandit} (MAB) decision making framework.
- We adapt the fairness framework of "treating similar individuals similarly" to this setting.
- Here, an `individual' corresponds to an arm and two arms are `similar' if they have a similar quality distribution.
- First, we adopt a {\em smoothness constraint} that if two arms have a similar quality distribution then the probability of selecting each arm should be similar.
Built on
Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
Earlier work this paper cites.
Action elimination and stopping conditions for the multi-armed and reinforcement learning problems
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Bayesian inference for plackett-luce ranking models
John Guiver and Edward Snelson · 2009
Earlier work this paper cites.
Label ranking methods based on the plackett-luce model
Weiwei Cheng, Eyke Hüllermeier, and Krzysztof J Dembczynski · 2010
Earlier work this paper cites.
Deterministic sequencing of exploration and exploitation for multi-armed bandit problems
Sattar Vakili, Keqin Liu, and Qing Zhao · 2011
Earlier work this paper cites.
Similar
Fairness through awareness
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Cited alongside, same era.
The k-armed dueling bandits problem
Yisong Yue, Josef Broder, Robert Kleinberg, and Thorsten Joachims · 2012
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Alexandra Chouldechova · 2016
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Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Rawlsian fairness for machine learning
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth
Cited in the paper.
Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth
Cited in the paper.
Then
Fair learning in Markovian environments
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaaron Roth · 2016
Later among the works it cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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f f -divergence inequalities
Igal Sason and Sergio Verdú · 2016
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Closest in time.
Subjective fairness: Fairness is in the eye of the beholder
Christos Dimitrakakis, Yang Liu, David Parkes, and Goran Radanovic · 2017
Closest in time.
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