Explainable AI is a crucial component for edge services, as it ensures reliable decision making based on complex AI models. Surrogate models are a prominent approach of XAI where human-interpretable models, such as a linear regression model, are trained to approximate a complex (black-box) model's predictions. This paper delves into the balance between the predictive accuracy of complex AI models and their approximation by surrogate ones, advocating that both these models benefit from being learned simultaneously. We derive a joint (bi-level) training scheme for both models and we introduce a new algorithm based on multi-objective optimization (MOO) to simultaneously minimize both the complex model's prediction error and the error between its outputs and those of the surrogate. Our approach leads to improvements that exceed 99% in the approximation of the black-box model through the surrogate one, as measured by the metric of Fidelity, for a compromise of less than 3% absolute reduction in the black-box model's predictive accuracy, compared to single-task and multi-task learning baselines. By improving Fidelity, we can derive more trustworthy explanations of the complex model's outcomes from the surrogate, enabling reliable AI applications for intelligent services at the network edge.

本研究解决了复杂人工智能模型的可解释性与预测准确性之间的平衡问题，提出了一种联合训练方案，通过多目标优化算法同时最小化复杂模型的预测误差及其替代模型的近似误差。研究结果表明，该方法显著提高了替代模型对黑箱模型的准确近似，超过99%的提升，同时仅使复杂模型的预测准确性减少不到3%。

基于替代模型的人工智能驱动边缘服务的联合可解释性-性能优化