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而复杂的问题,可以直接影响一个组织的竞争力和业绩。解释为什么员工离开一个组织是因为吸引和留住有才的员工需要高昂的成本和时间,因此,企业的目标是提高员工留用率,以最大限度地降低成本和最大限度地提高绩效。在人力资源管理的各个方面,包括自然流失预测,机器学习(ML)已经应用到人力资源管理的各个方面,包括自然流失预测,以便为企业提供关于如何防止有才华的员工辞职的积极措施的洞察力。在这种ML方法中,共同或深层神经网络报告了最佳绩效,而这些网络自然构成黑盒技术,因此难以解释。为了能够理解这些模型的逻辑推理,提出了几个解释框架。近年来,反事实解释方法引起了相当大的关注,因为这些方法可以用来解释和建议应采取的行动,以取得预期的结果。然而,目前反事实解释方法的重点是,如何优化对个别案例的修改,以实现理想的结果。在自然消耗问题中,必须能够预见到一个组织行动的效果,从本质上构成黑盒技术,因此,提出了几个解释框架框架。 反事实解释方法,我们把一个组织行动的重点放在一个组织中,而要避免一个组织对一个组织提出一个组织提出一个组织的概率解释,而要用一个最佳解释。</s>