Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring
Corneille Niyonkuru, Marcellin Atemkeng, Gabin Maxime Nguegnang, et al.
This paper presents a machine learning system to detect equipment problems in diesel generators used by telecom operators in Cameroon. The system combines multiple AI models with special techniques to handle imbalanced data, explain its decisions through SHAP analysis, and ensure fair predictions across different regions—achieving 99% accuracy while maintaining minimal bias. The researchers also discuss how to deploy this system for real-time monitoring in practice.
anomaly detectionexplainable AIfairness in MLclass imbalance