  Explaining deep learning models is of vital importance for understanding
artificial intelligence systems, improving safety, and evaluating fairness. To
better understand and control the CNN model, many methods for
transparency-interpretability have been proposed. However, most of these works
are less intuitive for human understanding and have insufficient human control
over the CNN model. We propose a novel method, Transparent and Controllable
Network Learning (TCNL), to overcome such challenges. Towards the goal of
improving transparency-interpretability, in TCNL, we define some concepts for
specific classification tasks through scientific human-intuition study and
incorporate concept information into the CNN model. In TCNL, the shallow
feature extractor gets preliminary features first. Then several concept feature
extractors are built right after the shallow feature extractor to learn
high-dimensional concept representations. The concept feature extractor is
encouraged to encode information related to the predefined concepts. We also
build the concept mapper to visualize features extracted by the concept
extractor in a human-intuitive way. TCNL provides a generalizable approach to
transparency-interpretability. Researchers can define concepts corresponding to
certain classification tasks and encourage the model to encode specific concept
information, which to a certain extent improves transparency-interpretability
and the controllability of the CNN model. The datasets (with concept sets) for
our experiments will also be released (https://github.com/bupt-ai-cz/TCNL).
