  Previous research in interpretable machine learning (IML) and explainable
artificial intelligence (XAI) can be broadly categorized as either focusing on
seeking interpretability in the agent's model (i.e., IML) or focusing on the
context of the user in addition to the model (i.e., XAI). The former can be
categorized as feature or instance attribution. Example- or sample-based
methods such as those using or inspired by case-based reasoning (CBR) rely on
various approaches to select instances that are not necessarily attributing
instances responsible for an agent's decision. Furthermore, existing approaches
have focused on interpretability and explainability but fall short when it
comes to accountability. Inspired in case-based reasoning principles, this
paper introduces a pseudo-metric we call Longitudinal distance and its use to
attribute instances to a neural network agent's decision that can be
potentially used to build accountable CBR agents.
