AINeutralarXiv – CS AI · 8h ago6/10
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Human Decision-Making with AI Assistance under Correlated Features
Researchers prove that when AI assists human decision-making with correlated features, stationary recommendation policies perform arbitrarily poorly, requiring instead an explore-then-commit strategy where AI initially recommends diverse options for human learning before committing to optimal selections. The study provides computational complexity results and algorithms for finding near-optimal policies, with exploration duration dependent on feature correlation strength.