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		<citationkey>LaranjeiraMotaSant:2021:WhIHa</citationkey>
		<title>Machine Learning Bias in Computer Vision: Why do I have to care?</title>
		<format>On-line</format>
		<year>2021</year>
		<numberoffiles>1</numberoffiles>
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		<author>Laranjeira, Camila,</author>
		<author>Mota, Virgínia Fernandes,</author>
		<author>Santos, Jefersson Alex dos,</author>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>COLTEC - Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<editor>Paiva, Afonso,</editor>
		<editor>Menotti, David,</editor>
		<editor>Baranoski, Gladimir V. G.,</editor>
		<editor>Proença, Hugo Pedro,</editor>
		<editor>Junior, Antonio Lopes Apolinario,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Pagliosa, Paulo,</editor>
		<editor>dos Santos, Thiago Oliveira,</editor>
		<editor>e Sá, Asla Medeiros,</editor>
		<editor>da Silveira, Thiago Lopes Trugillo,</editor>
		<editor>Brazil, Emilio Vital,</editor>
		<editor>Ponti, Moacir A.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Avila, Sandra,</editor>
		<e-mailaddress>virginiafernandesmota@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)</conferencename>
		<conferencelocation>Gramado (Virtual), Brazil</conferencelocation>
		<date>October 18th to October 22nd, 2021</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Tutorial</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>machine learning bias, computer vision, fairness in machine learning.</keywords>
		<abstract>Machine Learning bias is an issue with two main disadvantages. It compromises the quantitative performance of a system, and depending on the application, it may have a strong impact on society from an ethical viewpoint. In this work we inspect the literature on Computer Vision focusing on human-centered applications such as computer-aided diagnosis and face recognition to outline several forms of bias, bringing study cases for a more thorough inspection of how this issue takes form in the field of machine learning applied to images. We conclude with proposals from the literature on how to solve, or at least minimize, the impacts of bias.</abstract>
		<language>en</language>
		<targetfile>SIBGRAPI2021_Tutorial_MachineLearningBias.pdf</targetfile>
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