Practitioners are increasingly leveraging technology to enhance decision-making and improve efficiency. One area where technology has made significant strides is in algorithmic decision-making for personnel selection. This promises to offer increased standardization, time savings, and more reliable predictions of future performance.
However, many practitioners remain hesitant to adopt these tools, preferring to rely on their own intuition for hiring decisions. As such, this article explores a novel approach called autonomy-enhancing algorithmic procedures (AEAPs). These approaches are designed to improve the acceptance of algorithmic tools by allowing practitioners to maintain a sense of control in the decision-making process. Specifically, hiring managers are invited to use their judgment and expertise to change how an algorithm processes information, ultimately influencing which job candidates are selected.
AUTONOMY-ENHANCING ALGORITHMIC PROCEDURES
Researchers (Neumann et al., 2024) found that AEAPs led to more favorable evaluations from stakeholders compared to using a prescribed algorithm. Additionally, stakeholders viewed decision-makers more positively when they had the ability to adjust the predictions of an algorithm or even design their own algorithm, rather than strictly following a preset algorithm. Most notably, the use of AEAPs significantly improved the ability to predict future job success compared to intuition-based methods.
ORGANIZATIONAL TAKEAWAYS
The findings suggest that decision-makers using AEAPs were still perceived as responsible for and in control of the hiring process. Additionally, AEAPs led to more reliable predictions, making them a valuable tool for personnel selection. Based on these insights, the authors recommend the following:
- Create decision-making tools that allow practitioners to adjust predictor weights or outcomes when using algorithms. This could help hiring managers maintain a sense of control and improve perceptions of competence.
- Provide autonomy-enhancing features to address decision-makers’ concerns about stakeholder perceptions. This may encourage wider adoption of algorithmic systems.
Neumann, M., Niessen, S. M., Linde, M., Tendeiro, J. N., & Meijer, R. R. (2023). “Adding an egg” in algorithmic decision making: improving stakeholder and user perceptions, and predictive validity by enhancing autonomy. European Journal of Work and Organizational Psychology, 1–18.
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