Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and consequently on the outcome. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. The results indicate that AEC is more robust and stable against the impact of collinearity when it explains AI models compared with the state of arts XAI method.

Artificial intelligence techniques including machine learning models have shown success in variety of domains. This is more evident with complex models including deep learning. However, such success accompanied by vagueness around how the models work, the internal mechanism and making a decision. Explainable Artificial intelligence (XAI) emerged as a new field of research to uncover the mystery around how the complex models work. The ultimate aim of XAI is to make the complex models more transparent, trustworthy and understandable even by lay-persons with no technical background. XAI could come in different forms including heatmaps applied to images, significant concepts to the model when making a decision, informative features with tabular data, one-feature effect on the outcome, fuzzy logic rules, textual explanation through images captioning, uncertainty quantification and much more. There are several factors affect the XAI forms including the used data and the model. This paper is dedicated to review and group the current XAI methods in the literature based on the outcome form. In addition, the paper discusses the XAI groups, how they work, strengths and weaknesses. Our paper shows that although e XAI methods have been used extensively within the research context, their employability in real life problems especially in sensitive domains might be unreasonable in the current stage.

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.