Employee attrition is an important and complex problem that can directly affect an organisation's competitiveness and performance. Explaining the reasons why employees leave an organisation is a key human resource management challenge due to the high costs and time required to attract and keep talented employees. Businesses therefore aim to increase employee retention rates to minimise their costs and maximise their performance. Machine learning (ML) has been applied in various aspects of human resource management including attrition prediction to provide businesses with insights on proactive measures on how to prevent talented employees from quitting. Among these ML methods, the best performance has been reported by ensemble or deep neural networks, which by nature constitute black box techniques and thus cannot be easily interpreted. To enable the understanding of these models' reasoning several explainability frameworks have been proposed. Counterfactual explanation methods have attracted considerable attention in recent years since they can be used to explain and recommend actions to be performed to obtain the desired outcome. However current counterfactual explanations methods focus on optimising the changes to be made on individual cases to achieve the desired outcome. In the attrition problem it is important to be able to foresee what would be the effect of an organisation's action to a group of employees where the goal is to prevent them from leaving the company. Therefore, in this paper we propose the use of counterfactual explanations focusing on multiple attrition cases from historical data, to identify the optimum interventions that an organisation needs to make to its practices/policies to prevent or minimise attrition probability for these cases.

翻译：员工流失是一个重要且复杂的问题，会直接影响组织的竞争力和绩效。由于吸引和留住人才需要高昂的成本和时间，解释员工离开组织的原因成为人力资源管理的关键挑战。因此，企业旨在提高员工留存率，以最小化成本并最大化绩效。机器学习已应用于人力资源管理的多个方面，包括流失预测，为组织提供如何主动预防人才离职的见解。在这些机器学习方法中，集成模型或深度神经网络报告了最佳性能，但这些方法本质上是黑箱技术，难以解释。为了理解这些模型的推理过程，已提出了多种可解释性框架。近年来，反事实解释方法因其能解释并推荐实现预期结果的行动而受到广泛关注。然而，当前的反事实解释方法侧重于优化对个体案例的更改以实现预期结果。在员工流失问题中，能够预见组织行为对员工群体的影响至关重要，目标是阻止他们离职。因此，本文提出利用历史数据中的多个流失案例进行反事实解释，以识别组织需要对其实践/政策采取的最优干预措施，从而防止或最小化这些案例的流失概率。