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Estatística
Título: EVALUATING APPROACHES FOR DEVELOPERS ETHICAL REASONING AND COMMUNICATION ABOUT MACHINE LEARNING MODELS
Autor: JOSE LUIZ NUNES
Colaborador(es): SIMONE DINIZ JUNQUEIRA BARBOSA - Orientador
CLARISSE SIECKENIUS DE SOUZA - Coorientador
Catalogação: 30/NOV/2021 Língua(s): ENGLISH - UNITED STATES
Tipo: TEXT Subtipo: THESIS
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.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=56260&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=56260&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.56260
Resumo:
Machine learning algorithms have become widespread for a wide array of tasks. However, there is still no established way to deal with the ethical issues involved in their development and design. Some techniques have been proposed in the literature to support the reflection and/or documentation of the design and development of machine learning models, including ethical considerations, such as: (i) Model Cards and (ii) the Extended Metacommunication Template. We conducted a qualitative study to evaluate the use of these tools. We present our results concerning the use of the Model Card by participants, with the objective of understanding how these actors interacted with the relevant tool and the ethical dimension of their reflections during our interviews. Our goal is to improve and support techniques for developers to disclose information about their models and reflect ethically about the systems they design. Furthermore, we aim to contribute to the development of a more ethically informed and fairer use of machine learning.
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