1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/45CTJL8 |
Repository | sid.inpe.br/sibgrapi/2021/09.06.15.19 |
Last Update | 2021:09.06.21.28.34 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2021/09.06.15.19.20 |
Metadata Last Update | 2022:09.10.00.16.17 (UTC) administrator |
DOI | 10.1109/SIBGRAPI54419.2021.00010 |
Citation Key | LaranjeiraMotaSant:2021:WhIHa |
Title | Machine Learning Bias in Computer Vision: Why do I have to care? |
Format | On-line |
Year | 2021 |
Access Date | 2024, May 05 |
Number of Files | 1 |
Size | 9767 KiB |
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2. Context | |
Author | 1 Laranjeira, Camila 2 Mota, Virgínia Fernandes 3 Santos, Jefersson Alex dos |
Affiliation | 1 Universidade Federal de Minas Gerais 2 COLTEC - Universidade Federal de Minas Gerais 3 Universidade Federal de Minas Gerais |
Editor | Paiva, 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 Address | virginiafernandesmota@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) |
Conference Location | Gramado, RS, Brazil (virtual) |
Date | 18-22 Oct. 2021 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Tutorial |
History (UTC) | 2021-09-06 21:28:35 :: virginiafernandesmota@gmail.com -> administrator :: 2021 2022-03-03 04:41:59 :: administrator -> menottid@gmail.com :: 2021 2022-03-03 12:29:51 :: menottid@gmail.com -> administrator :: 2021 2022-09-10 00:16:17 :: administrator -> :: 2021 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
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. |
Arrangement | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Machine Learning Bias... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45CTJL8 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45CTJL8 |
Language | en |
Target File | SIBGRAPI2021_Tutorial_MachineLearningBias.pdf |
User Group | virginiafernandesmota@gmail.com |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS |
Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 4 sid.inpe.br/banon/2001/03.30.15.38.24 2 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume |
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