@inproceedings{10.1145/3583780.3615497,
author = {Mohiuddin, Karishma and Alam, Mirza Ariful and Alam, Mirza Mohtashim and Welke, Pascal and Martin, Michael and Lehmann, Jens and Vahdati, Sahar},
title = {Retention is All You Need},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615497},
doi = {10.1145/3583780.3615497},
abstract = {Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist HR departments in interpreting the predictions provided by machine learning models. In our experiments, we employ eight machine learning models to provide predictions. We further process the results achieved by the best-performing model by the SHAP explainability process and use the SHAP values to generate natural language explanations which can be valuable for HR. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions.},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {4752–4758},
numpages = {7},
keywords = {employee attrition and retention, machine learning models, decision support system, interpretable prediction, explainable AI, natural language generation, business intelligence},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}