Balancing Explainability-Accuracy of Complex Models
Poushali Sengupta Yan Zhang Sabita Maharjan Frank Eliassen
Explainability of AI models is an important topic that can have a significant
impact in all domains and applications from autonomous driving to healthcare.
The existing approaches to explainable AI (XAI) are mainly limited to simple
machine learning algorithms, and the research regarding the
explainability-accuracy tradeoff is still in its infancy especially when we are
concerned about complex machine learning techniques like neural networks and
deep learning (DL). In this work, we introduce a new approach for complex
models based on the co-relation impact which enhances the explainability
considerably while also ensuring the accuracy at a high level. We propose
approaches for both scenarios of independent features and dependent features.
In addition, we study the uncertainty associated with features and output.
Furthermore, we provide an upper bound of the computation complexity of our
proposed approach for the dependent features. The complexity bound depends on
the order of logarithmic of the number of observations which provides a
reliable result considering the higher dimension of dependent feature space
with a smaller number of observations.