PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries Kaczmarek Majer, Katarzyna Díaz Rodríguez, Natalia Ana eXplainable Artificial Intelligence Linguistic summaries Granular computing Fuzzy linguistic descriptions Machine learning Neural networks Bipolar disorders We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are derived through the popular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization allows translating the explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic statements and for the imprecision related to the data labeling process by including additional domain knowledge in the form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones from patients with bipolar disorder and on publicly available mental health survey data. The experiments confirm that fuzzy linguistic summarization is an effective technique to support meta-analyses of the outputs of AI models. Also, PLENARY improves explainability by aggregating low-level attributes into high-level information granules, and by incorporating vague domain knowledge into a multi-task sequential and compositional multilayer perceptron. SHAP explanations translated into fuzzy linguistic summaries significantly improve understanding of the predictive modelling process and its outputs. 2023-01-20T09:15:56Z 2023-01-20T09:15:56Z 2022-10-08 journal article Katarzyna Kaczmarek-Majer... [et al.]. PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries, Information Sciences, Volume 614, 2022, Pages 374-399, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2022.10.010] https://hdl.handle.net/10481/79174 10.1016/j.ins.2022.10.010 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier