AI, Take the Wheel: What Drives Delegation and Trust in Human–Computer Cooperative Question Answering?

AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment.Thus, improving human-AI collaboration requires that we understand when, why, and how humans decide to rely on AI.We study two reliance decisions: delegating a task to AI without seeing its output (whether AI is used) and evaluating AI suggestions to decide whether to adopt them (how AI output shapes final decisions).Both matter for effective collaboration, yet prior work lacks naturalistic experiments capturing both patterns for the same users.We address this gap by studying collaborative human-AI teams competing in a question-answering game in which humans can choose when and how to work with AI agents to win.Our 24 matches pair 23 expert humans with 16 AI agents, capturing 387 delegation and 1440 adoption decisions.While human-AI collaboration performs better than either AI or humans alone, humans make suboptimal collaboration decisions, both under-relying on correct AI suggestions (3.7% of opportunities missed) and over-relying when AI misleads them (1.5%).Both parties contribute wrong answers: reported model confidence is near chance when humans and AI disagree, while confirmation bias drives higher under-reliance (60.7%) when an AI suggestion agrees with humans' initial incorrect answer.