Blame the Machine? Insights From an Experiment on Algorithm Aversion and Blame Avoidance in Computer-Aided Human Resource Management

Abstract

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Algorithms have become increasingly relevant in supporting human resource (HR) management, but their application may entail psychological biases and unintended side effects on employee behavior. This study examines the effect of the type of HR decision (i.e., promoting or dismissing staff) on the likelihood of delegating these HR decisions to an algorithm-based decision support system. Based on prior research on algorithm aversion and blame avoidance, we conducted a quantitative online experiment using a 2×2 randomly controlled design with a sample of N = 288 highly educated young professionals and graduate students in Germany. This study partly replicates and substantially extends the methods and theoretical insights from a 2015 study by Dietvorst and colleagues. While we find that respondents exhibit a tendency of delegating presumably unpleasant HR tasks (i.e., dismissals) to the algorithm—rather than delegating promotions—this effect is highly conditional upon the opportunity to pretest the algorithm, as well as individuals’ level of trust in machine-based and human forecast. Respondents’ aversion to algorithms dominates blame avoidance by delegation. This study is the first to provide empirical evidence that the type of HR decision affects algorithm aversion only to a limited extent. Instead, it reveals the counterintuitive effect of algorithm pretesting and the relevance of confidence in forecast models in the context of algorithm-aided HRM, providing theoretical and practical insights.

Keywords

• algorithm aversion
• blame avoidance
• human resource management
• algorithm-based decision support systems
• behavioral experimental research