Table 2.

Main features of the model-agnostic interpretability techniques used in this study

Technique	Global	Local	Advantages	Disadvantages
Feature Importance	✓		• Highly compressed global interpretation • Consider interactions between features	Unclear whether it can be used on training dataset or testing dataset
Partial Dependence Plot	✓		Intuitive and clear interpretation	Assumption of independence between features
Individual Conditional Expectation	✓		Intuitive and easy to understand	Plot can become overcrowded to understand
Feature Interaction	✓		Detects all interactions been features	Computationally expensive
Global Surrogate Models	✓		Easy to measure the goodness of your surrogate model using R-squared measure	Not clear what is the best cut-off for R-squared to trust the resulted surrogate model
Local Surrogate Model (LIME)		✓	• Short and comprehensible explanation. • Explains different types of data (tabular, text and image)	• Instability of the explanation • Very close points may have totally different explanations
Shapley Value Explanations		✓	Explanation is based on strong game theory theorem	Computationally very expensive