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Better Luck Next Time: About Robust Recourse in Binary Allocation Problems
In this work, we present the problem of algorithmic recourse for the setting of binary allocation problems. In this setting, the optimal allocation does not depend only on the prediction model and the individual’s features, but also on the current available resources, utility function used by the decision maker and other individuals currently applying for the resource. We provide a method for generating counterfactual explanations under separable utilities that are monotonically increasing with prediction scores. Here, we assume that we can translate probabilities of “success” together with some other parameters into utility, such that the problem can be phrased as a knapsack problem and solved by known allocation policies: optimal 0–1 knapsack and greedy. We use the two policies respectively in the use cases of loans and college admissions. Moreover, we address the problem of recourse invalidation due to changes in allocation variables, under an unchanged prediction model, by presenting a method for robust recourse under variables’ distributions. Finally, we empirically compare our method with perturbation-robust recourse and show that our method can provide higher validity at a lower cost.
Minimax-Bayes Reinforcement Learning
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution. One idea is to employ a worst-case prior. However, this is not as easy to specify in sequential decision making as in simple statistical estimation problems. This paper studies (sometimes approximate) minimax-Bayes solutions for various reinforcement learning problems to gain insights into the properties of the corresponding priors and policies. We find that while the worst-case prior depends on the setting, the corresponding minimax policies are more robust than those that assume a standard (i.e. uniform) prior.