Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book‐dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state‐of‐the‐art artificial intelligence‐based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding‐based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL‐based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts‐of‐speech information and word2vec‐based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%.

Artificial Intelligence can provide quite accurate predictions for critical applications (e.g., healthcare), but lacks the ability to explain its internal mechanism in most applications which require high interaction with humans. Even if many studies analyze machine learning models and their learning behavior and eventually provide an interpretation of the inner mechanics of these models, these studies often entail a simpler surrogate model, which generates explanations by producing a piece of interpretable information such as feature scores. The crucial caveat against these studies is the lack of human involvement in the design and evaluation of explanations, consequently giving rise to trust issues and lack of acceptance and understanding. To this end, we address this limitation by involving humans in the counterfactual explanation generation process which is enriched with user feedback, thus enhancing the automated explanations which are better aligned with user expectations. In this paper, we propose a user feedback based counterfactual explanation approach (FCE) for explainable Artificial Intelligence. In our work, we utilize feedback in two ways: first, to customize the explanations by providing the acceptable ranges in the feature space where to look for feasible counterfactuals, and second, to evaluate the generated explanations.

Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding.