  Explainable Artificial Intelligence (XAI) is a young but very promising field
of research. Unfortunately, the progress in this field is currently slowed down
by divergent and incompatible goals. We separate various threads tangled within
the area of XAI into two complementary cultures of human/value-oriented
explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI).
This position paper argues that the area of RED XAI is currently
under-explored, i.e., more methods for explainability are desperately needed to
question models (e.g., extract knowledge from well-performing models as well as
spotting and fixing bugs in faulty models), and the area of RED XAI hides great
opportunities and potential for important research necessary to ensure the
safety of AI systems. We conclude this paper by presenting promising challenges
in this area.
