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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/45CTJL8
Repositorysid.inpe.br/sibgrapi/2021/09.06.15.19
Last Update2021:09.06.21.28.34 (UTC) virginiafernandesmota@gmail.com
Metadatasid.inpe.br/sibgrapi/2021/09.06.15.19.20
Metadata Last Update2021:11.12.11.47.14 (UTC) administrator
Citation KeyLaranjeiraMotaSant:2021:WhIHa
TitleMachine Learning Bias in Computer Vision: Why do I have to care?
FormatOn-line
Year2021
Access Date2022, Jan. 22
Number of Files1
Size9767 KiB
Context area
Author1 Laranjeira, Camila
2 Mota, Virgínia Fernandes
3 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais
2 COLTEC - Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressvirginiafernandesmota@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2021-09-06 21:28:35 :: virginiafernandesmota@gmail.com -> administrator :: 2021
2021-11-12 11:47:14 :: administrator -> virginiafernandesmota@gmail.com :: 2021
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsmachine learning bias
computer vision
fairness in machine learning
AbstractMachine 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.
Arrangementurlib.net > SDLA > SIBGRAPI 2021 > Machine Learning Bias...
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data URLhttp://sibgrapi.sid.inpe.br/ibi/8JMKD3MGPEW34M/45CTJL8
zipped data URLhttp://sibgrapi.sid.inpe.br/zip/8JMKD3MGPEW34M/45CTJL8
Languageen
Target FileSIBGRAPI2021_Tutorial_MachineLearningBias.pdf
User Groupvirginiafernandesmota@gmail.com
Visibilityshown
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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e-Mail (login)virginiafernandesmota@gmail.com
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