  The increased interest in deep learning applications, and their
hard-to-detect biases result in the need to validate and explain complex
models. However, current explanation methods are limited as far as both the
explanation of the reasoning process and prediction results are concerned. They
usually only show the location in the image that was important for model
prediction. The lack of possibility to interact with explanations makes it
difficult to verify and understand exactly how the model works. This creates a
significant risk when using the model. The risk is compounded by the fact that
explanations do not take into account the semantic meaning of the explained
objects. To escape from the trap of static and meaningless explanations, we
propose a tool and a process called LIMEcraft. LIMEcraft enhances the process
of explanation by allowing a user to interactively select semantically
consistent areas and thoroughly examine the prediction for the image instance
in case of many image features. Experiments on several models show that our
tool improves model safety by inspecting model fairness for image pieces that
may indicate model bias. The code is available at:
http://github.com/MI2DataLab/LIMEcraft
