  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.
