Explaining black-box models in natural language through fuzzy linguistic summaries - Bipolar Disorder case study

Combining several methods such as neural networks, explainable AI, and fuzzy linguistics using data from patients with bipolar disorder disease, we can obtain really interesting results.Our current work called PLENARY (explaining bLack-box models in Natural Language thRough fuzzY linguistic summaries) could help to comprehend how acoustic parameters could affect on patient's condition.Our predictive model is generated by neural networks and it is based on two levels of labels associated with the data. Explanations of that models are developed using Shapley Additive exPlanations (SHAP). Obtained results are visualization of acoustic parameters' impact on all types of labels. The last step is translating those explanation results into natural language using fuzzy linguistics summarization.