Approaching Principles of XAI: A Systematization

Today's explainable artificial intelligence (XAI) landscape is the product of a long history of ever-changing artificial intelligence (AI) research and attempts to explain it. It can be vast and confusing. Our historical reconstruction of XAI developments relates AI improvements to their inevitable impact on explanation research. The reconstruction provides the basis for an analysis of the state of XAI and for discussing its future developments in our article and in general. We then propose a new taxonomy based on this historical reconstruction and current XAI approaches. It is a balanced mixture of detail and general applicability. It is therefore intended to be useful in a wide variety of contexts. The flowchart inspired nature of our taxonomy relates its dimensions not only to the XAI development process, but also to each other, creating an additional layer of structure. Given the historical reconstruction and our taxonomy, we are able to propose three principles: <i>computing edges</i>, <i>dimensionality reduction</i>, and <i>traceability/blaming</i>. These are capable of structuring the debate in a new way, as they are not intended to be just ideas that current approaches adhere to. We also propose two new principles for the future (<i>embedment</i> and <i>scientific testing</i>) that XAI approaches should adhere to in order to improve their explanations. Our findings provide a structured approach to the analysis and development of XAI methodologies. By integrating historical perspectives with state-of-the-art approaches, our research provides a basis for stimulating discussion about the principles that XAI follows and should follow in the future.