Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection

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Abstract
Although Natural Language Processing (NLP) has traditionally relied on inherently interpretable “white-box” techniques, such as rule-based algorithms, decision trees, hidden Markov models, and logistic regression, the adoption of Large Language Models (LLMs) and language embeddings (often considered “black-box”) has significantly reduced interpretability. This lack of transparency introduces considerable risks, including biases, which have become a major concern in the field of Artificial Intelligence (AI). This Ph.D. thesis addresses these critical gaps by proposing new resources that ensure interpretability and fairness in NLP models for automated fact-checking and hate speech detection tasks. Specifically, we introduce five benchmark datasets (HateBR, HateBRXplain, HausaHate, MOL, and FactNews), four novel post-hoc and self-explaining methods (SELFAR, SSA, B+M and SRA), and one web platform (NoHateBrazil) designed to improve the interpretability and fairness of hate speech detection. The proposed models outperform the existing baselines for Portuguese and Hausa, both underrepresented languages. This research contributes to ongoing discussions on responsible and explainable AI, bridging the gap between model performance and interpretability to achieve positive real-world social impact. Finally, this thesis has had a significant impact both nationally and internationally, receiving citations from prestigious universities and research institutes abroad, inspiring new M.Sc. and Ph.D. in Brazil, and being recognized with multiple awards, including the Google LARA, the Maria Carolina Monard AI Award, and as a finalist at the Brazilian Computer Society Thesis and Dissertation Award.

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