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作者：Balakrishnan, Maya; Ferreira, Kris Johnson; Tong, Jordan
作者单位：University of Texas System; University of Texas Dallas; Harvard University; University of Wisconsin System; University of Wisconsin Madison
摘要：Even if algorithms make better predictions than humans on average, humans may sometimes have private information that an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by algorithms in such situations? When deciding whether and how to override an algorithm's recommendations, we hypothesize that people are biased toward following na & iuml;ve advice-weighting (NAW) behavior; they take a weighted average betw...
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作者：Wang, Xiaoning; Wu, Lynn
作者单位：University of Texas System; University of Texas Dallas; University of Pennsylvania
摘要：Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese sta...
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作者：Wang, Hongchang; Zhang, Yingjie; Lu, Tian
作者单位：University of Texas System; University of Texas Dallas; Peking University; Arizona State University; Arizona State University-Tempe
摘要：Human-algorithm collaboration is becoming increasingly prevalent in the economy and society. However, this collaboration is not always fruitful, and in extreme cases, people become human borgs or totally averse to algorithms. The key to collaborative value is whether humans and algorithms can complement each other in decision making, but it is challenging for humans to disagree with algorithmic recommendations at the right time (i.e., to disagree when algorithms are wrong and not disagree when...
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作者：Snyder, Clare; Keppler, Samantha; Leider, Stephen
作者单位：University of Michigan System; University of Michigan
摘要：In algorithm-augmented service contexts where workers have decision authority, they face two decisions about the algorithm: whether to follow its advice and how quickly to do so. The pressure to work quickly increases with the speed of arriving customers. In this paper, we ask the following. How do workers use algorithms to manage system loads? With a laboratory experiment, we find that superior algorithm quality and high system loads increase participants' willingness to use their algorithm's...
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作者：Hu, Xiyang; Huang, Yan; Li, Beibei; Lu, Tian
作者单位：Arizona State University; Arizona State University-Tempe; Carnegie Mellon University; Carnegie Mellon University
摘要：Prior work on human-algorithmic bias has seen difficulty in empirically identifying the underlying mechanisms of bias because in a typical one-time decision-making scenario, different mechanisms generate the same patterns of observable decisions. In this study, leveraging a unique repeat decision-making setting in a high-stakes microlending context, we aim to uncover the underlying source, evolution dynamics, and associated impacts of bias. We first develop a structural econometric model of th...
