  Explainable artificial intelligence (XAI) is a set of tools and algorithms
that applied or embedded to machine learning models to understand and interpret
the models. They are recommended especially for complex or advanced models
including deep neural network because they are not interpretable from human
point of view. On the other hand, simple models including linear regression are
easy to implement, has less computational complexity and easy to visualize the
output. The common notion in the literature that simple models including linear
regression are considered as "white box" because they are more interpretable
and easier to understand. This is based on the idea that linear regression
models have several favorable outcomes including the effect of the features in
the model and whether they affect positively or negatively toward model output.
Moreover, uncertainty of the model can be measured or estimated using the
confidence interval. However, we argue that this perception is not accurate and
linear regression models are not easy to interpret neither easy to understand
considering common XAI metrics and possible challenges might face. This
includes linearity, local explanation, multicollinearity, covariates,
normalization, uncertainty, features contribution and fairness. Consequently,
we recommend the so-called simple models should be treated equally to complex
models when it comes to explainability and interpretability.
