  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 have a level of
complexity that renders them opaque black boxes, resulting in a notable lack of
transparency that hinders our ability to decipher their reasoning. 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 address that challenge, unraveling the complexity of black boxes
by providing elucidating explanations. Among the various XAI approaches,
feature attribution/importance 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. This
work introduces T-Explainer, a novel local additive attribution explainer based
on Taylor expansion. It has desirable properties, such as local accuracy and
consistency, making T-Explainer stable over multiple runs. We demonstrate
T-Explainer's effectiveness in quantitative benchmark experiments against
well-known attribution methods. Additionally, we provide several tools to
evaluate and visualize explanations, turning T-Explainer into a comprehensive
XAI framework.
