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Found 10 papers, 2 papers with code
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group.
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits
We study the tension that arises between two seemingly conflicting objectives in the horizon-unaware setting: a) maximizing the cumulative reward at any time based on current rewards of the arms, and b) ensuring that arms with better long-term rewards get sufficient opportunities even if they initially have low rewards.
Strategic Representation
In our main setting of interest, the system represents attributes of an item to the user, who then decides whether or not to consume.
Efficient Algorithms For Fair Clustering with a New Fairness Notion
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every cluster; i. e., a balance property.
Sleeping Combinatorial Bandits
An algorithm can select a subset of arms from the \emph{availability set} (sleeping bandits) and receive the corresponding reward along with semi-bandit feedback (combinatorial bandits).
Strategic Classification in the Dark
Strategic classification studies the interaction between a classification rule and the strategic agents it governs.
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare.
Ballooning Multi-Armed Bandits
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.
Achieving Fairness in the Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
Achieving Fairness in Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
