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dc.contributor.advisorBarone, Dante Augusto Coutopt_BR
dc.contributor.authorGuntzel, Maurício Hollerpt_BR
dc.date.accessioned2022-07-22T04:53:48Zpt_BR
dc.date.issued2022pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/245286pt_BR
dc.description.abstractIncreasingly, machine learning models perform high-stakes decisions in almost any do main. These models and the datasets - they are trained on– may be prone to exacerbating social disparities due to unmitigated fairness issues. For example, features representing different social groups are known as protected features– as stated by Equality Act of 2010; they correspond to one of these fairness issues. This work explores the impact of protected features on predictive models’ outcomes and their performance and fairness. We propose a knowledge-driven pipeline for detecting protected features and mitigating their effect. Protected features are defined based on metadata and are removed during the training phase of the models. Nevertheless, these protected features are merged into the output of the models to preserve the original dataset information and enhance explainability. We empirically study four machine learning models (i.e., KNN, Decision Tree, Neural Net work, and Naive Bayes) and datasets for fairness benchmarking (i.e., COMPAS, Adult Census Income, and Credit Card Default). The observed results suggest that the proposed pipeline preserves the models’ performance and facilitate the extraction of information of the models’ to use in fairness metrics.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.rightsOpen Accessen
dc.subjectAprendizado de máquinapt_BR
dc.subjectPipelineen
dc.subjectOleodutopt_BR
dc.subjectfairnessen
dc.subjectmachine learningen
dc.subjectBig datapt_BR
dc.subjectpositive outcomeen
dc.subjectgroup fairnessen
dc.subjectFairness Though Unawarenessen
dc.titleFairness in machine learning : an empirical experiment about protected features and their implicationspt_BR
dc.typeTrabalho de conclusão de graduaçãopt_BR
dc.contributor.advisor-coCôrtes, Eduardo Gabrielpt_BR
dc.identifier.nrb001146016pt_BR
dc.degree.grantorUniversidade Federal do Rio Grande do Sulpt_BR
dc.degree.departmentInstituto de Informáticapt_BR
dc.degree.localPorto Alegre, BR-RSpt_BR
dc.degree.date2022pt_BR
dc.degree.graduationCiência da Computação: Ênfase em Ciência da Computação: Bachareladopt_BR
dc.degree.levelgraduaçãopt_BR


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