Efficient XAI Techniques: A Taxonomic Survey

Abstract--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. 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. Despite the advancements in ML, providing instance requires a unique explainer during the derivation transparency in the models, particularly in deep neural of the explanation. In addition, the local explanation suffers networks (DNNs), remains a substantial challenge. The lack from extensive computational conditions due to the pending of transparency can lead to mistrust and skepticism of ML amounts of tested instances, where each instance requires model predictions, such as the block-box driving decisions massive permutation times to complete the importance score made by autopilots.