Keywords: explainable AI, counterfactual explanations, practicality, human-friendly explanations, user study
TL;DR: Evaluating how practical counterfactual explanations are to human subjects.
Abstract: Machine learning models are increasingly used for decisions that directly affect people’s lives. These models are often opaque, meaning that the people affected cannot understand how or why the decision was made. However, according to the General Data Protection Regulation, decision subjects have the right to an explanation. Counterfactual explanations are a way to make machine learning models more transparent by showing how attributes need to be changed to get a different outcome. This type of explanation is considered easy to understand and human-friendly. To be used in real life, explanations must be practical, which means they must go beyond a purely theoretical framework. Research has focused on defining several objective functions to compute practical counterfactuals. However, it has not yet been tested whether people perceive the explanations as such in practice. To address this, we contribute by identifying properties that explanations must satisfy to be practical for human subjects. The properties are then used to evaluate the practicality of two counterfactual explanation methods (CARE and WachterCF) by conducting a user study. The results show that human subjects consider the explanations by CARE (a multi-objective approach) to be more practical than the WachterCF (baseline) explanations. We also show that the perception of explanations differs depending on the classification task by exploring multiple datasets.