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Several strands of research have aimed to bridge the gap between artificialintelligence (AI) and human decision-makers in AI-assisted decision-making,where humans are the consumers of AI model predictions and the ultimatedecision-makers in high-stakes applications. However, people's perception andunderstanding are often distorted by their cognitive biases, such asconfirmation bias, anchoring bias, availability bias, to name a few. In thiswork, we use knowledge from the field of cognitive science to account forcognitive biases in the human-AI collaborative decision-making setting, andmitigate their negative effects on collaborative performance. To this end, wemathematically model cognitive biases and provide a general framework throughwhich researchers and practitioners can understand the interplay betweencognitive biases and human-AI accuracy. We then focus specifically on anchoringbias, a bias commonly encountered in human-AI collaboration. We implement atime-based de-anchoring strategy and conduct our first user experiment thatvalidates its effectiveness in human-AI collaborative decision-making. Withthis result, we design a time allocation strategy for a resource-constrainedsetting that achieves optimal human-AI collaboration under some assumptions.We, then, conduct a second user experiment which shows that our time allocationstrategy with explanation can effectively de-anchor the human and improvecollaborative performance when the AI model has low confidence and isincorrect.

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making