How to overcome algorithm aversion: Learning from mistakes
Taly Reich, Alex Kaju, Sam J. Maglio
Abstract
Abstract When consumers avoid taking algorithmic advice, it can prove costly to both marketers (whose algorithmic product offerings go unused) and to themselves (who fail to reap the benefits that algorithmic predictions often provide). In a departure from previous research focusing on when algorithm aversion proves more or less likely, we sought to identify and remedy one reason why it occurs in the first place. In seven pre‐registered studies, we find that consumers tend to avoid algorithmic advice on the often faulty assumption that those algorithms, unlike their human counterparts, cannot learn from mistakes, in turn offering an inroad by which to reduce algorithm aversion: highlighting their ability to learn. Process evidence, through both mediation and moderation, examines why consumers fail to trust algorithms that err across a variety of prediction domains and how different theory‐driven interventions can solve the practical problem of enhancing trust and consequential choice in algorithms.