  Modern, state-of-the-art Convolutional Neural Networks (CNNs) in computer
vision have millions of parameters. Thus, explaining the complex decisions of
such networks to humans is challenging. A technical approach to reduce CNN
complexity is network pruning, where less important parameters are deleted. The
work presented in this paper investigates whether this technical complexity
reduction also helps with perceived explainability. To do so, we conducted a
pre-study and two human-grounded experiments, assessing the effects of
different pruning ratios on CNN explainability. Overall, we evaluated four
different compression rates (i.e., CPR 2, 4, 8, and 32) with 37 500 tasks on
Mechanical Turk. Results indicate that lower compression rates have a positive
influence on explainability, while higher compression rates show negative
effects. Furthermore, we were able to identify sweet spots that increase both
the perceived explainability and the model's performance.
