AI term aversion in career decision-making: contextual reactions to algorithmic labels

As technological advancements continue to shape decision support systems, algorithmic tools are increasingly utilised in career-related contexts. This research investigates how terminology influences individuals’ acceptance of algorithmic decision aids in career decision-making. We introduce the concept of algorithm term aversion, examining whether users' preferences differ depending on how algorithms are labelled. Across three studies (N = 459), we explored preferences for algorithmically driven agents in various contexts: job applications (Study 1), future career advice (Study 2), and career advancement (Study 3). Findings reveal a consistent aversion to the term “artificial intelligence” across all contexts and outcome measures. However, broader algorithm aversion did not consistently emerge, suggesting terminology plays a critical role in user acceptance. Understanding how users respond to algorithmic terminology can inform the design of more user-friendly decision support systems, thereby enhancing the integration of AI into sensitive decision-making domains, such as career decisions.