Backward Compatibility in Attributive Explanation and Enhanced Model Training Method
<|reference_start|>Backward Compatibility in Attributive Explanation and Enhanced Model Training Method: Model update is a crucial process in the operation of ML/AI systems. While updating a model generally enhances the average prediction performance, it also significantly impacts the explanations of predictions. In real-world applications, even minor changes in explanations can have detrimental consequences. To tackle this issue, this paper introduces BCX, a quantitative metric that evaluates the backward compatibility of feature attribution explanations between pre- and post-update models. BCX utilizes practical agreement metrics to calculate the average agreement between the explanations of pre- and post-update models, specifically among samples on which both models accurately predict. In addition, we propose BCXR, a BCX-aware model training method by designing surrogate losses which theoretically lower bounds agreement scores. Furthermore, we present a universal variant of BCXR that improves all agreement metrics, utilizing L2 distance among the explanations of the models. To validate our approach, we conducted experiments on eight real-world datasets, demonstrating that BCXR achieves superior trade-offs between predictive performances and BCX scores, showcasing the effectiveness of our BCXR methods.<|reference_end|>
@article{matsuno2024backward,
    title={Backward Compatibility in Attributive Explanation and Enhanced Model
  Training Method},
    author={Ryuta Matsuno},
    journal={arXiv preprint arXiv:2408.02298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02298},
    primaryClass={cs.LG}
}