Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

TL;DR

This study empirically investigates the perceived tradeoff between machine learning model performance and explainability from an end user perspective, challenging the common assumption of an inherent tradeoff.

Contribution

It provides empirical evidence that the performance-explainability tradeoff is less strict than assumed and highlights the importance of explanation type in user perception.

Findings

End users do not perceive a strict tradeoff between performance and explainability.

The tradeoff is situational and influenced by data complexity.

Explanation type significantly affects user perception of AI explanations.

Abstract

Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user's perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception.