Green AI for Sustainable Employee Attrition Prediction: Balancing Energy Efficiency and Predictive Accuracy
DOI:
https://doi.org/10.15662/IJARCST.2025.0803004Keywords:
Green AI, Employee Attrition, Machine Learning, Energy Efficiency, Predictive Analytics, Human Resources, SustainabilityAbstract
This study investigates the application of Green Artificial Intelligence (AI) principles to employee attrition prediction models, aiming to reduce computational energy consumption while maintaining comparable predictive accuracy to conventional approaches. The research addresses a critical gap at the intersection of sustainable AI and human resource management. A mixed factorial design was employed, evaluating six machine learning algorithms (Logistic Regression, Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbors, Decision Tree) in both conventional and Green AI-optimized versions, across three feature selection methods. Experiments were conducted on the IBM HR Analytics Employee Attrition & Performance dataset (N=1,470), with energy consumption (kWh/Wh) measured using OpenZmeter, CodeCarbon, and CarbonTracker, alongside standard performance metrics (Accuracy, F1-score, AUC-ROC). Results indicate that Green AI models achieved a significant average energy reduction of 44.8% during training and 35.6% during inference compared to conventional models. Crucially, these substantial energy savings were realized with only minimal and statistically non-significant differences in predictive performance (e.g., F1-score: Green AI M=0.62 vs. Conventional M=0.64, p=.07). Specific Green AI strategies, including model parameter optimization and feature selection, effectively reduced computational load while preserving performance. Optimized XGBoost models notably demonstrated a strong balance of high accuracy and reduced energy consumption. This research provides compelling empirical evidence that sustainable AI practices are feasible and effective in HR analytics, offering a viable pathway for organizations to mitigate their carbon footprint and operational costs without compromising the quality of predictive insights.
References
1. Akinode, L., & Bada, O. (2022). Employee Attrition Prediction Using Machine Learning Algorithms. International Journal of Computer Applications, 184(27), 35-40.
2. Al-Suradi, M. T., Alshurideh, M. T., Al-Hawary, S. I. S., Al-Zu’bi, Z. M., & Al-Qudah, A. (2023). Machine Learning Models for Predicting Employee Attrition: A Data Science Perspective. Data Management, 6(2), 669.
3. Anthony, L. F. W., Kanding, B., & Selvan, R. (2020). Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models. ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems. arXiv:2007.03051.
4. Aquino-Brítez, S., García-Sánchez, P., Ortiz, A., & Aquino-Brítez, D. (2025). Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools. Sensors, 25(3), 846. https://doi.org/10.3390/s25030846
5. Bolón-Canedo, V., Alonso-González, C. J., & Sánchez-Maroño, N. (2024). Green AI and Cloud Computing Initiatives. In AI and Cloud Computing for Sustainability. IGI Global.
6. Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020). Zeroq: A novel zero shot quantization framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13169–13178).
7. Cascio, W. F., & Boudreau, J. W. (2016). Investing in people: Financial impact of human resource initiatives (2nd ed.). Pearson Education.
8. Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G., & Friday, A. (2021). The real climate and transformative impact of ICT: A critique of estimates, trends and regulations. Patterns, 2(9), 100340. https://doi.org/10.1016/j.patter.2021.100340
9. Garcia-Martin, E., Lavesson, N., Grahn, H., Casalicchio, E., & Tärneberg, W. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), ECML PKDD 2018 Workshops (Vol. 869, pp. 243–255). Springer, Cham. https://doi.org/10.1007/978-3-030-13453-2_20
10. Grealey, J., Lannelongue, L., & Inouye, M. (2022). Data-Centric Green Artificial Intelligence: A Survey. IEEE Transactions on Artificial Intelligence, PP(99), 1-18.
11. Gupta, S., Vinuesa, R., & Qadir, J. (2023). Artificial intelligence and sustainable development goals nexus via four...source 102171.
12. Haque, M., Paralkar, T. A., Rajguru, S., Goyal, A. A., Patil, T., & Upreti, K. (2025). Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability-Relating Factors. International Research Journal of Multidisciplinary Scope (IRJMS), 6(2), 862-873. https://doi.org/10.47857/irjms.2025.v06i02.03512
13. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. Journal of Machine Learning Research, 21, 1-43.
14. Hohman, F., Wang, C., Lee, J., Görtler, J., Moritz, D., Bigham, J. P., Ren, Z., Foret, C., Shan, Q., & Zhang, X. (2024). Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3642628
15. Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530-545.
16. IBM. (2021). IBM HR Analytics Employee Attrition & Performance. Kaggle. Retrieved from https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
17. Iparraguirre-Villanueva, P. J., Oyinloye, P. O., & Campbell, J. (2024). Employee Attrition and its Impact on National Cash Flow: A Case Study of the United States Economy in 2024. International Journal of Economic Policy, 4(3), 46-62.
18. Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the Carbon Emissions of Machine Learning. Tackling Climate Change with Machine Learning Workshop, NeurIPS. arXiv:1910.09700.
19. Lannelongue, L., Grealey, J., & Inouye, M. (2021). Green algorithms: Quantifying the carbon footprint of computation. Advanced Science, 8(12), 2100707.
20. Liu, Y., Wang, X., & Jia, W. (2025). Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies. arXiv preprint arXiv:2501.03265.
21. Ma, X., Liu, W., Zhao, C., & Tukhvatulina, L. R. (2025). Can Large Language Model Predict Employee Attrition? ICCSMT 2024: 2024 5th International Conference on Computer Science and Management Technology. https://doi.org/10.1145/3708036.3708229
22. Mukherjee, M., & Khushi, M. (2021). SMOTE-ENC: A Novel SMOTE-Based Method to Generate Synthetic Data for Nominal and Continuous Features. Applied System Innovation, 4(1), 18. https://doi.org/10.3390/asi4010018
23. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
24. Patterson, D., Gonzalez, J., Le, Q., Liang, C., M., Rothchild, D., So, D., Texier, M., & Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. IEEE Computer, 55(7).
25. Patil, T., Haque, M., Paralkar, T. A., Rajguru, S., Goyal, A. A., & Upreti, K. (2025). Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability-Relating Factors. International Research Journal of Multidisciplinary Scope (IRJMS), 6(2), 862-873. https://doi.org/10.47857/irjms.2025.v06i02.03512
26. Raza, A., Munir, K., Almutairi, M., Younas, F., & Fareed, M. M. S. (2022). Predicting employee attrition using machine learning approaches. Applied Sciences, 12(13), 6424. https://doi.org/10.3390/app12136424
27. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
28. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243.
29. Tabbakh, A., Al Amin, L., Islam, M., Mahmud, G. I., Chowdhury, I. K., & Mukta, M. S. H. (2024). Towards sustainable AI: A comprehensive framework for Green AI. Discover Sustainability, 5(1), 408. https://doi.org/10.1002/sd.3221
30. Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020). The computational limits of deep learning. arXiv preprint arXiv:2007.05558.
31. Verdecchia, R., Sallou, J., & Cruz, L. (2023). A systematic review of Green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1493
32. Yoo, S.-R., Shin, J.-W., & Choi, S.-H. (2025). Machine learning vehicle fuel efficiency prediction. Scientific Reports, 15(1), 14815. https://doi.org/10.1038/s41598-025-96999-0
33. Yu, H., Gu, L., Li, X., Gao, T., Feng, B., Zhou, Q., Xing, J.-C., Deng, Z., Al-Hajj, R., Assi, A. H., Neji, B., Al Barakeh, Z., Pinto, G., Wang, Z., Roy, A., Capozzoli, A., Qiao, M., Yan, S., Tang, X., … Grolinger, K. (2022). A Transfer Learning Framework for Predictive Energy-Related Scenarios in Smart Buildings. IEEE Transactions on Industry Applications, PP(99), 1-1.
34. Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2019). Employee Turnover Prediction with Machine Learning: A Reliable Approach. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing (Vol. 869). Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_56
