@InBook{Müller2022,
  pages     = {311--330},
  title     = {Towards a Taxonomy for Explainable AI in Computational Pathology},
  publisher = {Springer International Publishing},
  year      = {2022},
  author    = {M{\"u}ller, Heimo and Kargl, Michaela and Plass, Markus and Kipperer, Bettina and Brcic, Luka and Regitnig, Peter and Gei{\ss}ler, Christian and K{\"u}ster, Tobias and Zerbe, Norman and Holzinger, Andreas},
  editor    = {Chen, Fang and Zhou, Jianlong},
  address   = {Cham},
  isbn      = {978-3-030-72188-6},
  abstract  = {This chapter aims to provide a common understanding of some important aspects and factors involved in building a human-centred AI laboratory for explainability and causability measures in medicine generally and in digital pathology specifically. This is highly relevant to the broader topic of human-driven AI. The benefit for the reader with general AI interest is to get insight into an important part of real-world medicine. The chapter also shows how explainable AI benefits pathology in particular and medicine in general and demonstrates the importance of human-driven AI. First, we need to define and understand what we mean by AI, what algorithms we are talking about and how these may be applied in medicine, specifically in histopathology. Second, we need to understand, who are the stakeholders using these AI applications, and what are their aims and requirements. One of the central claims of this chapter is that all the discussed aspects support the hypothesis that explainability is one of the most important points to establish trustworthiness to AI generally and to computational pathology specifically.},
  booktitle = {Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership},
  doi       = {10.1007/978-3-030-72188-6_15},
}