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Interact."],"published-print":{"date-parts":[[2022,3,30]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3512930","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T16:54:59Z","timestamp":1649350499000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":121,"title":["Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making"],"prefix":"10.1145","volume":"6","author":[{"given":"Charvi","family":"Rastogi","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}]},{"given":"Yunfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Twitter, New York City, NY, USA"}]},{"given":"Dennis","family":"Wei","sequence":"additional","affiliation":[{"name":"IBM T. 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( 2020 ). Addressing cognitive biases in augmented business decision systems. arXiv preprint arXiv:2009.08127 . Baudel, T., Verbockhaven, M., Roy, G., Cousergue, V., and Laarach, R. (2020). Addressing cognitive biases in augmented business decision systems. arXiv preprint arXiv:2009.08127."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1965479"},{"key":"e_1_2_1_9_1","first-page":"21","article-title":"]totrustbucinca2021Buc cinca, Z., Malaya, M. B., and Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions canreduce overreliance on ai in ai-assisted decision-making","volume":"5","year":"2021","unstructured":"ca , 2021 ]totrustbucinca2021Buc cinca, Z., Malaya, M. B., and Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions canreduce overreliance on ai in ai-assisted decision-making . Proc. ACM Hum.-Comput. Interact. 5, CSCW1 , 5 : 21 . ca et al., 2021]totrustbucinca2021Buc cinca, Z., Malaya, M. B., and Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions canreduce overreliance on ai in ai-assisted decision-making. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, 5:21.","journal-title":"Proc. ACM Hum.-Comput. Interact. 5, CSCW1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.1526"},{"key":"e_1_2_1_11_1","volume-title":"Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behavior","author":"Chambon V.","year":"2020","unstructured":"Chambon , V. , Th\u00e9ro , H. , Vidal , M. , Vandendriessche , H. , Haggard , P. , and Palminteri , S . ( 2020 ). Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behavior . Chambon, V., Th\u00e9ro, H., Vidal, M., Vandendriessche, H., Haggard, P., and Palminteri, S. (2020). Information about action outcomes differentially affects learning from self-determined versus imposed choices. 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