Owing to advancements in artificial intelligence (AI) and specifically in machine learning, information technology (IT) systems can support humans in an increasing number of tasks. Yet, previous research indicates that people often prefer human support to support by an IT system, even if the latter provides superior performance – a phenomenon called algorithm aversion. A possible cause of algorithm aversion put forward in literature is that users lose trust in IT systems they become familiar with and perceive to err, for example, making forecasts that turn out to deviate from the actual value. Therefore, this paper evaluates the effectiveness of demonstrating an AI-based system’s ability to learn as a potential countermeasure against algorithm aversion in an incentive-compatible online experiment. The experiment reveals how the nature of an erring advisor (i.e., human vs. algorithmic), its familiarity to the user (i.e., unfamiliar vs. familiar), and its ability to learn (i.e., non-learning vs. learning) influence a decision maker’s reliance on the advisor’s judgement for an objective and non-personal decision task. The results reveal no difference in the reliance on unfamiliar human and algorithmic advisors, but differences in the reliance on familiar human and algorithmic advisors that err. Demonstrating an advisor’s ability to learn, however, offsets the effect of familiarity. Therefore, this study contributes to an enhanced understanding of algorithm aversion and is one of the first to examine how users perceive whether an IT system is able to learn. The findings provide theoretical and practical implications for the employment and design of AI-based systems.

中文翻译：

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看我改进——算法厌恶和展示学习能力

由于人工智能 (AI) 特别是机器学习的进步，信息技术 (IT) 系统可以支持人类完成越来越多的任务。然而，先前的研究表明，人们往往更喜欢人类支持而不是 IT 系统的支持，即使后者提供了卓越的性能——这种现象称为算法厌恶。文献中提出的算法厌恶的一个可能原因是用户对他们熟悉的 IT 系统失去信任并认为会出错，例如，做出的预测与实际值有偏差。因此，本文评估了在激励兼容的在线实验中展示基于 AI 的系统的学习能力作为对抗算法厌恶的潜在对策的有效性。该实验揭示了错误顾问的性质（即人类与算法）、其对用户的熟悉程度（即，不熟悉与熟悉）及其学习能力（即，非学习与学习）如何影响决策者依赖顾问对客观和非个人决策任务的判断。结果表明，对不熟悉的人类和算法顾问的依赖没有差异，但对熟悉的人类和算法顾问的依赖存在差异。然而，展示顾问的学习能力会抵消熟悉的影响。因此，这项研究有助于加深对算法厌恶的理解，并且是第一个研究用户如何看待 IT 系统是否能够学习的研究之一。