Explainable AI without Interpretable Model
  Explainability has been a challenge in AI for as long as AI has existed. With
the recently increased use of AI in society, it has become more important than
ever that AI systems would be able to explain the reasoning behind their
results also to end-users in situations such as being eliminated from a
recruitment process or having a bank loan application refused by an AI system.
Especially if the AI system has been trained using Machine Learning, it tends
to contain too many parameters for them to be analysed and understood, which
has caused them to be called `black-box' systems. Most Explainable AI (XAI)
methods are based on extracting an interpretable model that can be used for
producing explanations. However, the interpretable model does not necessarily
map accurately to the original black-box model. Furthermore, the
understandability of interpretable models for an end-user remains questionable.
The notions of Contextual Importance and Utility (CIU) presented in this paper
make it possible to produce human-like explanations of black-box outcomes
directly, without creating an interpretable model. Therefore, CIU explanations
map accurately to the black-box model itself. CIU is completely model-agnostic
and can be used with any black-box system. In addition to feature importance,
the utility concept that is well-known in Decision Theory provides a new
dimension to explanations compared to most existing XAI methods. Finally, CIU
can produce explanations at any level of abstraction and using different
vocabularies and other means of interaction, which makes it possible to adjust
explanations and interaction according to the context and to the target users.
