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Artificial Intelligence (AI) has a tremendous impact on the unexpected growthof technology in almost every aspect. AI-powered systems are monitoring anddeciding about sensitive economic and societal issues. The future is towardsautomation, and it must not be prevented. However, this is a conflictingviewpoint for a lot of people, due to the fear of uncontrollable AI systems.This concern could be reasonable if it was originating from considerationsassociated with social issues, like gender-biased, or obscure decision-makingsystems. Explainable AI (XAI) is recently treated as a huge step towardsreliable systems, enhancing the trust of people to AI. Interpretable machinelearning (IML), a subfield of XAI, is also an urgent topic of research. Thispaper presents a small but significant contribution to the IML community,focusing on a local-based, neural-specific interpretation process applied totextual and time-series data. The proposed methodology introduces newapproaches to the presentation of feature importance based interpretations, aswell as the production of counterfactual words on textual datasets. Eventually,an improved evaluation metric is introduced for the assessment ofinterpretation techniques, which supports an extensive set of qualitative andquantitative experiments.

LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information