AI-Assisted Classification under Correlation Neglect and Trust

When does an AI assistant improve human decision-making, and when does it hurt? We consider the setting in which a human worker makes a binary decision by observing her own signal and receiving a binary or probabilistic recommendation from an AI. We develop a behavioral model which considers both her belief regarding the overlap of her signal with the AI as well as the trust she has in the AI when considering its recommendation. The model yields a phase diagram in AI capability and signal overlap, partitioning the parameter space into complementarity, impairment, and automation regions. A worker who neglects signal overlap (correlation neglect) can experience performance loss when incorporating AI into her decision and is eventually dominated by AI alone as AI capability grows, with the complementarity range narrowing as overlap rises. Trust calibration shifts region boundaries: undertrust shrinks the impairment region but expands automation, while overtrust expands impairment but extends complementarity to be preferred at higher AI capability levels. Applying the framework to ChatBench (643 workers, GPT-4o and Llama-3.1-8b), AI assistance raises accuracy by 21 percentage points, but workers undertrust the AI by roughly a factor of four. The model yields design insights linking AI deployment choices, worker behavioral parameters, and outcome quality.