  In the field of eXplainable AI (XAI), robust ``blackbox'' algorithms such as
Convolutional Neural Networks (CNNs) are known for making high prediction
performance. However, the ability to explain and interpret these algorithms
still require innovation in the understanding of influential and, more
importantly, explainable features that directly or indirectly impact the
performance of predictivity. A number of methods existing in literature focus
on visualization techniques but the concepts of explainability and
interpretability still require rigorous definition. In view of the above needs,
this paper proposes an interaction-based methodology -- Influence Score
(I-score) -- to screen out the noisy and non-informative variables in the
images hence it nourishes an environment with explainable and interpretable
features that are directly associated to feature predictivity. We apply the
proposed method on a real world application in Pneumonia Chest X-ray Image data
set and produced state-of-the-art results. We demonstrate how to apply the
proposed approach for more general big data problems by improving the
explainability and interpretability without sacrificing the prediction
performance. The contribution of this paper opens a novel angle that moves the
community closer to the future pipelines of XAI problems.
