用联合优化提升边缘AI可解释性，逼近精度超99%且性能损失低于3%
Joint Explainability-Performance Optimization With Surrogate Models for AI-Driven Edge Services
- 双模型同步训练，黑箱模型与代理模型共同优化
- 代理模型对黑箱模型的逼近度（Fidelity）超99%，性能损失<3%
- 适合需要高可信度解释的边缘智能服务场景
可解释AI是边缘服务的关键，确保基于复杂AI模型的可靠决策。代理模型是XAI的重要方法，通过可解释模型（如线性回归）逼近复杂黑箱模型的预测结果。本文研究黑箱模型预测精度与其代理模型逼近精度之间的平衡，主张二者应同时学习。我们提出一种双层联合训练方案，并设计基于多目标优化（MOO）的新算法，同时最小化黑箱模型的预测误差和其输出与代理模型输出之间的差异。实验表明，该方法在仅牺牲不足3%绝对精度的前提下，使代理模型对黑箱模型的逼近度（以Fidelity衡量）超过99%，显著优于单任务和多任务学习基线。提升Fidelity后，可从代理模型中提取更可信的解释，支持边缘网络中可靠AI应用。
原文摘要 · Abstract (English)
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.
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