Machine learning with real-world HR data: mitigating the trade-off between predictive performance and transparency
本研究利用德国联邦机构的纵向员工离职数据，揭示了机器学习在预测性能与透明度之间的权衡，并提出事后解释方法来缓解这一矛盾，对HRM实践者和研究者有参考价值。
Machine Learning (ML) algorithms offer a powerful tool for capturing multifaceted relationships through inductive research to gain insights and support decision-making in practice.This study contributes to understanding the dilemma whereby the more complex ML becomes, the more its value proposition can be compromised by its opacity.Using a longitudinal dataset on voluntary employee turnover from a German federal agency, we provide evidence for the underlying trade-off between predictive performance and transparency for ML, which has not been found in similar Human Resource Management (HRM) studies using artificially simulated datasets.We then propose measures to mitigate this trade-off by demonstrating the use of post-hoc explanatory methods to extract local (employee-specific) and global (organisation-wide) predictor effects.After that, we discuss their limitations, providing a nuanced perspective on the circumstances under which the use of post-hoc explanatory methods is justified.Namely, when a 'transparencyby-design' approach with traditional linear regression is not sufficient to solve HRM prediction tasks, the translation of complex ML models into human-understandable visualisations is required.As theoretical implications, this paper suggests that we can only fully understand the multi-layered HR phenomena explained to us by real-world data if we incorporate ML-based inductive methods together with traditional deductive methods.