Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection
Resumo
Esta tese de doutorado avança o estado da arte em Inteligência Artificial (IA) e Processamento de Linguagem Natural (PLN) ao abordar dois desafios críticos: a escassez de Corpora de alta qualidade para línguas subrepresentadas no Sul Global e a interpretabilidade limitada dos modelos de linguagem modernos em tarefas com alto impacto social. A pesquisa concentra-se na detecção de discurso de ódio e na verificação automática de fatos, propondo novos conjuntos de dados, métodos explicáveis e benchmarks de avaliação para aprimorar a transparência, a equidade e a confiabilidade dos sistemas de PLN. A tese apresenta sete conjuntos de dados benchmarks para o português brasileiro e a língua hausa: HateBR, HateBRXplain, HateBRMoralXplain, MFTCXplain, MOL, FactNews e HausaHate. Esses recursos apoiam pesquisas em detecção de discurso de ódio, raciocínio moral das LLMs e verificação automática de fatos. Com base nesses recursos, a tese propõe cinco novos métodos interpretáveis e explicáveis: Sentence-Level Factual Reasoning (SELFAR), Social Stereotype Analysis (SSA), Contextual BoW with Human Rationales and Feature Optimization (B+M), Supervised Rational Attention (SRA) e Supervised Moral Rational Attention (SMRA). Essas abordagens integram justificativas humanas (human rationales), bem como evidências morais e factuais, às arquiteturas neurais, aprimorando tanto o desempenho preditivo quanto a qualidade das explicações. Experimentos realizados em múltiplos conjuntos de dados demonstram melhorias consistentes em relação a modelos neurais de referência, ao mesmo tempo em que produzem explicações mais fiéis e melhor alinhadas ao raciocínio humano. Além de suas contribuições metodológicas, a tese gerou impacto científico e social significativo. Seus conjuntos de dados e métodos foram adotados por pesquisadores de universidades e institutos de pesquisa no Brasil e no exterior, incluindo a Microsoft Research e outras instituições de referência, contribuindo para novos estudos em Inteligência Artificial Explicável (XAI), Processamento de Linguagem Natural Confiável (Trustworthy NLP) e tecnologias de linguagem socialmente responsáveis para línguas sub-representadas. A qualidade e o impacto desta pesquisa também foram reconhecidos por diversos prêmios nacionais e internacionais, incluindo o Google LARA, o Prêmio de Melhor Tese do PROPOR, o Prêmio Maria Carolina Monard de Melhor Tese em Inteligência Artificial, o Trevisan Prize – AI for Good da Universidade Bocconi na Itália, ACL D&I Awards, além de duas indicações a Prêmios de Teses da Sociedade Brasileira de Computação (SBC), nas categorias de Ciência da Computação e de Multimídia, Hipermídia e Web.
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Publicado
19/10/2026
Como Citar
VARGAS, Francielle; PARDO, Thiago A. S.; BENEVENUTO, Fabricio.
Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection. In: CONCURSO DE TESES E DISSERTAÇÕES EM INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (CTDIAC), 15. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 115-134.
DOI: https://doi.org/10.5753/ctdiac.2026.33597.
