  Mounting evidence in explainability for artificial intelligence (XAI)
research suggests that good explanations should be tailored to individual tasks
and should relate to concepts relevant to the task. However, building task
specific explanations is time consuming and requires domain expertise which can
be difficult to integrate into generic XAI methods. A promising approach
towards designing useful task specific explanations with domain experts is
based on compositionality of semantic concepts. Here, we present a novel
approach that enables domain experts to quickly create concept-based
explanations for computer vision tasks intuitively via natural language.
Leveraging recent progress in deep generative methods we propose to generate
visual concept-based prototypes via text-to-image methods. These prototypes are
then used to explain predictions of computer vision models via a simple
k-Nearest-Neighbors routine. The modular design of CoProNN is simple to
implement, it is straightforward to adapt to novel tasks and allows for
replacing the classification and text-to-image models as more powerful models
are released. The approach can be evaluated offline against the ground-truth of
predefined prototypes that can be easily communicated also to domain experts as
they are based on visual concepts. We show that our strategy competes very well
with other concept-based XAI approaches on coarse grained image classification
tasks and may even outperform those methods on more demanding fine grained
tasks. We demonstrate the effectiveness of our method for human-machine
collaboration settings in qualitative and quantitative user studies. All code
and experimental data can be found in our GitHub
$\href{https://github.com/TeodorChiaburu/beexplainable}{repository}$.
