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

• URL: http://arxiv.org/abs/2010.07938v2
• Date: Mon, 4 Apr 2022 22:42:04 GMT
• Title: Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
• Authors: Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett
• Abstract summary: We use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting. We focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration.
• Score: 46.625616262738404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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