A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification

In the era of deep learning and the rapid advancement of powerful Artificial Intelligence (AI) models, consisting of numerous layers and millions of parameters, the demand for understanding the decision-making process of black box models is on the rise. Explainable AI is a growing trend that seeks to uncover the inner workings of AI systems through computational analysis, shedding light on the decision-making process and has been applied across a variety of data types such as video [1], text [2], AIS [3] and causal [4] and genomic data [5], and applications such as art [6], medicine [7], finance [8] and education [9]. Explainability methods can be broadly divided into model agnostic or model free and model specific approaches. Model-agnostic methods can be applied to any trained machine learning model regardless of the learning mechanism and model architecture. Rule based methods [10] and sensitivity analysis are two common approaches from this category.