Group-Sensitive Offline Contextual Bandits
NeutralArtificial Intelligence
A new paper on arXiv discusses the challenges of offline contextual bandits, which are used to learn policies from historical data without online interaction. The study highlights how optimizing for overall rewards can inadvertently create disparities among different groups, raising important questions about fairness in resource allocation. This research is significant as it addresses the need for equitable solutions in machine learning applications, ensuring that all groups benefit fairly from technological advancements.
— Curated by the World Pulse Now AI Editorial System




