Minimizing Model Misclassification Using Regularized Loss Interpretability
Elyes Manai, Mohamed Mejri, Jaouhar Fattahi · 2024
In the field of machine learning, the increasing use of complex deep learning models like Deep Neural Networks is leading to a decrease in the transparency of decision-making processes. This presents significant challenges, especially in sensitive applications where specific misclassifications can have serious consequences. The complexity of these models makes it challenging to target specific enhancements, such as rectifying misclassifications within a subgroup of the dataset or improving the classification rate of a particular label group. Achieving these improvements often necessitates extensive testing with the hope of attaining the desired results. This paper presents an innovative approach to proactively reduce misclassifications between specific pairs of labels in multiclass scenarios with already trained models and with minimal decrease in overall performance. The focus on already trained models is to reduce testing and retraining costs, enabling teams to efficiently and affordably audit their models and enhance robustness and fairness before deploying them into production. By modifying loss functions to assign penalties to undesirable misclassifications, our approach directs the optimization process to prioritize the reduction of specific critical errors. Our experimental results demonstrate the effectiveness of this strategy in reducing targeted misclassifications while maintaining or improving overall model performance metrics such as accuracy, precision, and F1 score. This study contributes to the field of Explainable Artificial Intelligence (XAI) by offering a cheap, fast and practical tool for embedding fairness and interpretability directly into the model training process, paving the way for more transparent and accountable AI systems.