Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods typically suffer from a high computational complexity problem, which discourages the deployment of real-time systems to meet the time-demanding requirements of real-world scenarios. Although many approaches have been proposed to improve the efficiency of XAI methods, a comprehensive understanding of the achievements and challenges is still needed. To this end, in this paper we provide a review of efficient XAI. Specifically, we categorize existing techniques of XAI acceleration into efficient non-amortized and efficient amortized methods. The efficient non-amortized methods focus on data-centric or model-centric acceleration upon each individual instance. In contrast, amortized methods focus on learning a unified distribution of model explanations, following the predictive, generative, or reinforcement frameworks, to rapidly derive multiple model explanations. We also analyze the limitations of an efficient XAI pipeline from the perspectives of the training phase, the deployment phase, and the use scenarios. Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.

翻译：近年来，将可解释人工智能算法部署至实际应用的需求日益增长。然而，传统可解释人工智能方法普遍存在高计算复杂性问题，这阻碍了实时系统部署以满足现实场景对时效性的需求。尽管已有诸多方法致力于提升可解释人工智能的效率，但对其成就与挑战的系统性理解仍显不足。为此，本文对高效可解释人工智能进行了综述。具体而言，我们将现有可解释人工智能加速技术分为高效非摊销方法与高效摊销方法两大类。非摊销方法聚焦于数据驱动或模型驱动的单实例加速，而摊销方法则通过学习模型解释的统一分布（基于预测、生成或强化学习框架）来快速推导多组模型解释。我们还从训练阶段、部署阶段及使用场景三个维度分析了高效可解释人工智能管线的局限性。最后，我们总结了将可解释人工智能加速方法部署至现实场景所面临的挑战，包括如何在忠实度与效率之间取得平衡，以及不同加速方法的选型问题。