Top-performing Artificial Intelligence models often operate as black boxes. Explainable AI (XAI) can increase transparency, but its evaluation is currently hindered by a lack of annotated explanation data and agreed-upon validation standards. We propose a framework for evaluating the faithfulness of explanations in Portuguese hate speech detection. Our approach is based on the premise that a faithful explanation should identify features whose removal degrades a model’s performance. We follow a three-step process: (i) prediction on the original input; (ii) identification and removal of explanatory keywords; and (iii), prediction on the modified input, with performance differences used as an evaluation signal. We conduct experiments using ensemble classifiers, multiple keyword selection strategies, and SHAP and LIME as XAI methods. In addition, Large Language Models (LLMs) are explored both as classifiers and as explainers. Results demonstrate that removing explanatory keywords degrades model performance more than random word removal, indicating explanation faithfulness. Notably, SHAP and LIME consistently provided more faithful explanations than LLM-generated or manual alternatives, although impact depends on the keyword selection strategy. These findings highlight the importance of standardised, unsupervised evaluation protocols for XAI and the faithfulness limitations of current generative LLM explanations.

Customer-support services increasingly rely on automation, whether fully or with human intervention. Despite optimising resources, this may result in mechanical protocols and lack of human interaction, thus reducing customer loyalty. Our goal is to enhance interpretability and provide guidance in communication through novel tools for easier analysis of message trends and sentiment variations. Monitoring these contributes to more informed decision-making, enabling proactive mitigation of potential issues, such as protocol deviations or customer dissatisfaction. We propose a generic approach for dialogue flow discovery that leverages clustering techniques to identify dialogue states, represented by related utterances. State transitions are further analyzed to detect prevailing sentiments. Hence, we discover sentiment-aware dialogue flows that offer an interpretability layer to artificial agents, even those based on black-boxes, ultimately increasing trustworthiness. Experimental results demonstrate the effectiveness of our approach across different dialogue datasets, covering both human-human and human-machine exchanges, applicable in task-oriented contexts but also to social media, highlighting its potential impact across various customer-support settings.