  Several strands of research have aimed to bridge the gap between artificial
intelligence (AI) and human decision-makers in AI-assisted decision-making,
where humans are the consumers of AI model predictions and the ultimate
decision-makers in high-stakes applications. However, people's perception and
understanding are often distorted by their cognitive biases, such as
confirmation bias, anchoring bias, availability bias, to name a few. In this
work, we use knowledge from the field of cognitive science to account for
cognitive biases in the human-AI collaborative decision-making setting, and
mitigate their negative effects on collaborative performance. To this end, we
mathematically model cognitive biases and provide a general framework through
which researchers and practitioners can understand the interplay between
cognitive biases and human-AI accuracy. We then focus specifically on anchoring
bias, a bias commonly encountered in human-AI collaboration. We implement a
time-based de-anchoring strategy and conduct our first user experiment that
validates its effectiveness in human-AI collaborative decision-making. With
this result, we design a time allocation strategy for a resource-constrained
setting that achieves optimal human-AI collaboration under some assumptions.
We, then, conduct a second user experiment which shows that our time allocation
strategy with explanation can effectively de-anchor the human and improve
collaborative performance when the AI model has low confidence and is
incorrect.

Human-Computer Interaction;Machine Learning