The Dependency Dilemma: How Machine Learning Decision Aids can Undermine Skill Growth

Abstract Advances in Machine Learning (ML) have led organizations to increasingly implement ML decision aids to enhance employees’ decision-making performance. While such systems can improve organizational efficiency in many contexts, they may inadvertently impact the development of human decision-making skills. Drawing on cognitive theories, this study examines how the use of ML decision aids impact skill development and performance. Using a novel experimental design tailored to address organizational challenges and endogeneity concerns, the study identifies causal effects of reliance on ML predictions on skill development in decision making. Specifically, it is demonstrated that reliance on ML predictions in a prediction-making task can hinder the development of critical decision-making skills, resulting in significant performance drops when the system becomes unavailable. Furthermore, it is found that the extent of trust in the system's predictions strongly influences the severity of this skill deficit. These findings highlight the need for thoughtful integration of ML decision aids, emphasizing the importance of balancing reliance with skill retention to mitigate risks associated with temporary or permanent system disruptions.