@conference {1525259,
	title = {Corporate Social Responsibility via Multi-Armed Bandits},
	booktitle = {AI for Social Good Workshop},
	year = {2020},
	abstract = {
	We propose a multi-armed bandit setting where each arm corresponds to a subpopulation, and pulling an arm is equivalent to granting an opportunity to this subpopulation. In this setting the decision-maker{\textquoteright}s fairness policy governs the number of opportunities each subpopulation should receive, which typically depends on the (unknown) reward from granting an opportunity to this subpopulation. The decision-maker can decide whether to provide these opportunities or pay a predefined monetary value for every withheld opportunity. The decision-maker{\textquoteright}s objective is to maximize her utility, which is the sum of rewards minus the cost of withheld opportunities. We provide a no-regret algorithm that maximizes the decisionmaker{\textquoteright}s utility and complement our analysis with an almost-tight lower bound. Full version of the paper is available at https://tinyurl.com/y7s9avud.



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},
	author = {Tom Ron and Omer Ben-Porat and Uri Shalit}
}
