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In this paper, we run two methods of explanation, namely LIME and Grad-CAM,on a convolutional neural network trained to label images with the LEGO bricksthat are visible in them. We evaluate them on two criteria, the improvement ofthe network's core performance and the trust they are able to generate forusers of the system. We find that in general, Grad-CAM seems to outperform LIMEon this specific task: it yields more detailed insight from the point of viewof core performance and 80\% of respondents asked to choose between them whenit comes to the trust they inspire in the model choose Grad-CAM. However, wealso posit that it is more useful to employ these two methods together, as theinsights they yield are complementary.

Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task