Título: | EXPLAINABLE ARTIFICIAL INTELLIGENCE IN MACHINE LEARNING | ||||||||||||
Autor(es): |
GABRIEL BANAGGIA DE SOUZA |
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Colaborador(es): |
SIMONE DINIZ JUNQUEIRA BARBOSA - Orientador |
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Catalogação: | 04/SET/2024 | Língua(s): | ENGLISH - UNITED STATES |
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Tipo: | TEXT | Subtipo: | SENIOR PROJECT | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=67842@1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=67842@2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.67842 | ||||||||||||
Resumo: | |||||||||||||
Explainable Artificial Intelligence aims to assist in opening the blackbox of opaque algorithms, especially present in machine learning systems,so they can earn due trustworthiness. This work employs a systematic literature review in order to understand how the concepts of explainability andinterpretability, among others, are defined in this context. It offers a structured vocabulary to better comprehend and classify artificial intelligence systems and models, as well as the techniques used to make them more intelligible. Inspired by Human-Centered Computing notions, it becomes crucial to consider the audience to which explanations are owed, emphasizing that it is composed of a community of heterogeneous users.
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