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Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making by Charvi Rastogi; Yunfeng Zhang; Dennis Wei; Kush R. Varshney; Amit Dhurandhar; Richard Tomsett is a Computer Science article available to read on EtoBox.

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

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-

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Author

Charvi Rastogi; Yunfeng Zhang; Dennis Wei; Kush R. Varshney; Amit Dhurandhar; Richard Tomsett

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)