“怎么让他们留下来？“--员工离职的各种反事实陈述
"How to make them stay?" -- Diverse Counterfactual Explanations of Employee Attrition
摘要：
员工流失是一个重要而复杂的问题，可以直接影响组织的竞争力和绩效。解释员工离开组织的原因是一个关键的人力资源管理挑战，因为吸引和留住有才华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保留率，以最大限度地降低成本并最大限度地提高绩效。机器学习（ML）已被应用于人力资源管理的各个方面，包括流失预测，为企业提供关于如何防止有才华的员工离职的积极措施的见解。在这些ML方法中，集成或深度神经网络报告了最好的性能，其本质上构成了黑盒技术，因此无法轻松解释。为了能够理解这些模型的推理，已经提出了几个可解释性框架。近年来，反事实解释方法引起了相当大的关注，因为它们可以用来解释和建议采取行动，以获得预期的结果。然而，目前的反事实解释方法侧重于优化对个别情况的改变，以达到预期的结果。在自然减员问题中，重要的是能够预见组织的行动对一群员工的影响，其目标是防止他们离开公司。因此，在本文中，我们建议使用反事实的解释，重点是从历史数据中的多个减员案件，以确定最佳的干预措施，一个组织需要使其实践/政策，以防止或尽量减少这些案件的减员概率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
