The development of machine learning applications has increased significantly
in recent years, motivated by the remarkable ability of learning-powered
systems to discover and generalize intricate patterns hidden in massive
datasets. Modern learning models, while powerful, often exhibit a level of
complexity that renders them opaque black boxes, resulting in a notable lack of
transparency that hinders our ability to decipher their decision-making
processes. Opacity challenges the interpretability and practical application of
machine learning, especially in critical domains where understanding the
underlying reasons is essential for informed decision-making. Explainable
Artificial Intelligence (XAI) rises to meet that challenge, unraveling the
complexity of black boxes by providing elucidating explanations. Among the
various XAI approaches, feature attribution/importance XAI stands out for its
capacity to delineate the significance of input features in the prediction
process. However, most existing attribution methods have limitations, such as
instability, when divergent explanations may result from similar or even the
same instance. In this work, we introduce T-Explainer, a novel local additive
attribution explainer based on Taylor expansion endowed with desirable
properties, such as local accuracy and consistency, while stable over multiple
runs. We demonstrate T-Explainer's effectiveness through benchmark experiments
with well-known attribution methods. In addition, T-Explainer is developed as a
comprehensive XAI framework comprising quantitative metrics to assess and
visualize attribution explanations.

可解释人工智能（Explainable Artificial Intelligence）通过提供准确、一致且稳定的解释，解决机器学习中黑盒模型的透明度问题，其中基于泰勒展开的 T-Explainer 成为了一种有效的特征归因方法。