LITERATURE REVIEW
There is a great need to understand how things occur. Hence the need for comprehensibility or interpretability. Interpretation can be said to be the action of explaining something to know how that aspect works, why the part behaves in a particular manner and any impact that the element in question can have on attributes depending on it. In machine learning, interpretability is highly important nowadays as these models run vital critical processes worldwide in financial, academic, and political scenarios.
Why is interpretability important? From the literature reviewed, interpretability is essential based on TWO reasons. One aspect of being a human being is that we need to put an explanation to every event that occurs in our lives. This need thus has driven the urge to be able to explain certain causal circumstances and why they informed a particular decision. This need is equally essential in machine learning as we need to explain why a particular model chooses a specific prediction instead of another. Thus the interpretability of machine learning models can aid human beings in learning new aspects and also find meaning in their decisions. (C Molnar 2019)
The other is handling uncertainty and biases in decisions. Much research has developed new models to handle uncertainty and bias in machine learning models. Certainly, interpretability is an important aspect that can be used to address discrimination and uncertainty. Machine learning models pick preferences from training data caused by missing values in some datasets, which leads to uncertainty in these models when it comes to predicting the outcomes based on the training data they have. This makes the models unstable and gives outrageous predictions that cannot be explained. Thus interpretability can be used to handle this issue and offer a reprieve for models and their developers. (C Molnar 2019)
Review of literature by Christoph Molnar in his book ”Interpretable machine learning,” we see that interpretable machine learning models can be classified according to FOUR fronts. The figure below illustrates the classification of interpretable machine learning models.