A general framework for scientifically inspired explanations in AI
  Explainability in AI is gaining attention in the computer science community
in response to the increasing success of deep learning and the important need
of justifying how such systems make predictions in life-critical applications.
The focus of explainability in AI has predominantly been on trying to gain
insights into how machine learning systems function by exploring relationships
between input data and predicted outcomes or by extracting simpler
interpretable models. Through literature surveys of philosophy and social
science, authors have highlighted the sharp difference between these generated
explanations and human-made explanations and claimed that current explanations
in AI do not take into account the complexity of human interaction to allow for
effective information passing to not-expert users. In this paper we instantiate
the concept of structure of scientific explanation as the theoretical
underpinning for a general framework in which explanations for AI systems can
be implemented. This framework aims to provide the tools to build a
"mental-model" of any AI system so that the interaction with the user can
provide information on demand and be closer to the nature of human-made
explanations. We illustrate how we can utilize this framework through two very
different examples: an artificial neural network and a Prolog solver and we
provide a possible implementation for both examples.
