@InProceedings{LaranjeiraMotaSant:2021:WhIHa,
               author = "Laranjeira, Camila and Mota, Virg{\'{\i}}nia Fernandes and 
                         Santos, Jefersson Alex dos",
          affiliation = "{Universidade Federal de Minas Gerais } and {COLTEC - Universidade 
                         Federal de Minas Gerais } and {Universidade Federal de Minas 
                         Gerais}",
                title = "Machine Learning Bias in Computer Vision: Why do I have to care?",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "machine learning bias, computer vision, fairness in machine 
                         learning.",
             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.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00010",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00010",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CTJL8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CTJL8",
           targetfile = "SIBGRAPI2021_Tutorial_MachineLearningBias.pdf",
        urlaccessdate = "2024, May 05"
}