This study investigates how the terminology used to describe algorithmic systems—specifically “algorithm” versus “chatbot”—influences user preferences, behavior, and the dynamics of human–artificial intelligence (AI) decision support. Additionally, it examines whether the objectivity of the task moderates this effect. Across two preregistered studies, participants consistently preferred the term “algorithm” over “chatbot” and were more likely to follow advice labeled as coming from an “algorithm” than from a “chatbot,” regardless of task objectivity. In the first study, 115 participants indicated their preference for either human advice or various AI system terms across two tasks: predicting stock prices (objective) and measuring romantic attraction (subjective). In the second study, 568 participants were randomly assigned to one of several AI system terms and tasks, assessing their preference and advice-taking, the latter measured by the Weight of Advice. The findings reveal that terminology significantly impacts user preference, behavior, and trust in algorithmically generated outputs, illustrating the broader implications for human cognitive and emotional responses in algorithmically supported decision-making contexts. These results underscore the importance of language in shaping human–AI interactions and provide practical guidance for organizations aiming to foster AI adoption through careful choice of terminology. They also extend research on algorithm aversion and human–AI interaction by offering behavioral evidence for critical data studies, showing that algorithmic labels influence trust and compliance beyond system performance.
Chacon et al. (Sun,) studied this question.