  The black-box nature of machine learning models limits their use in
case-critical applications, raising faithful and ethical concerns that lead to
trust crises. One possible way to mitigate this issue is to understand how a
(mispredicted) decision is carved out from the decision boundary. This paper
presents a human-in-the-loop approach to explain machine learning models using
verbatim neighborhood manifestation. Contrary to most of the current
eXplainable Artificial Intelligence (XAI) systems, which provide hit-or-miss
approximate explanations, our approach generates the local decision boundary of
the given instance and enables human intelligence to conclude the model
behavior. Our method can be divided into three stages: 1) a neighborhood
generation stage, which generates instances based on the given sample; 2) a
classification stage, which yields classifications on the generated instances
to carve out the local decision boundary and delineate the model behavior; and
3) a human-in-the-loop stage, which involves human to refine and explore the
neighborhood of interest. In the generation stage, a generative model is used
to generate the plausible synthetic neighbors around the given instance. After
the classification stage, the classified neighbor instances provide a
multifaceted understanding of the model behavior. Three intervention points are
provided in the human-in-the-loop stage, enabling humans to leverage their own
intelligence to interpret the model behavior. Several experiments on two
datasets are conducted, and the experimental results demonstrate the potential
of our proposed approach for boosting human understanding of the complex
machine learning model.
