Authors:
André Artelt
1
;
2
and
Andreas Gregoriades
3
Affiliations:
1
Faculty of Technology, Bielefeld University, Bielefeld, Germany
;
2
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
;
3
Department of Management, Entrepreneurship and Digital Business, Cyprus University of Technology, Limassol, Cyprus
Keyword(s):
Employee Attrition Prediction, Counterfactual Explanations, Explainable Machine Learning.
Abstract:
Employee attrition is an important and complex problem that can directly affect an organisation’s competitive- ness 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. Busi- nesses therefore aim to increase employee retention rates to minimise their costs and maximise their perfor- mance. 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 em- ployees 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 to either explain individual cases
using local interpretation approaches or provide global explanations describ- ing the overall logic of the predictive model. 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 ex- planations 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. The proposed technique is applied on an employee attrition dataset, used to train binary classifiers. Counterfactual explanations are generated based on multiple attrition cases, thus, providing recommendations to the human resource department on how to prevent attrition.
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