Abstract

Due to the widespread use of artificial intelligence and machine learning models across numerous domains that directly impact individuals’ lives, it is essential to ensure that these models are making their decisions in a non-discriminatory manner. That is, biases encoded in the underlying training data are not reflected in the output of the model. This is, however, often hard to control since the predictive accuracy of these models come at the cost of interpretability.
The purpose of this thesis is to investigate and apply methods for interpreting black-box machine learning models to bring more transparency into their decision-making process by analyzing both the direct and indirect influence of input features on model predictions. We begin by studying the theoretical background of the SHAP (SHapley Additive exPlanations) framework, Shapley values, and their implementation in measuring direct influence. We explore the SHAP Python package for both local and global explanations and visualization of direct feature influence on both synthetic and real-world datasets. We then transition to studying indirect influence using the Disentangled Influence Audit procedure, which uses adversarial training to learn disentangled latent representations for auditing hidden dependencies. After presenting all the background information, we implement this procedure and evaluate these methods through numerical experiments on synthetic functions and real-world datasets including the Adult Income and the Montréal Housing datasets. In addition to these experiments, we also examine how “data-hungry” these auditing methods are by examining their convergence as a function of the number of samples required. Our results demonstrate the importance of auditing both direct and indirect pathways of influence to promote interpretability and fairness in complex models.