  Artificial Intelligence (AI) has a tremendous impact on the unexpected growth
of technology in almost every aspect. AI-powered systems are monitoring and
deciding about sensitive economic and societal issues. The future is towards
automation, and it must not be prevented. However, this is a conflicting
viewpoint for a lot of people, due to the fear of uncontrollable AI systems.
This concern could be reasonable if it was originating from considerations
associated with social issues, like gender-biased, or obscure decision-making
systems. Explainable AI (XAI) is recently treated as a huge step towards
reliable systems, enhancing the trust of people to AI. Interpretable machine
learning (IML), a subfield of XAI, is also an urgent topic of research. This
paper presents a small but significant contribution to the IML community,
focusing on a local-based, neural-specific interpretation process applied to
textual and time-series data. The proposed methodology introduces new
approaches to the presentation of feature importance based interpretations, as
well as the production of counterfactual words on textual datasets. Eventually,
an improved evaluation metric is introduced for the assessment of
interpretation techniques, which supports an extensive set of qualitative and
quantitative experiments.
