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Beating the bias in facial recognition technology
In 2019, San Francisco became the first US city to ban facial recognition technology (FRT), specifically vetoing its use by police and other agencies(1). Since then, several other American cities have implemented their own similar FRT bans, with Boston's city councillors(2) explicitly highlight...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier Ltd.
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575263/ http://dx.doi.org/10.1016/S0969-4765(20)30122-3 |
| _version_ | 1783597776270000128 |
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| author | Lunter, Jan |
| author_facet | Lunter, Jan |
| author_sort | Lunter, Jan |
| collection | PubMed |
| description | In 2019, San Francisco became the first US city to ban facial recognition technology (FRT), specifically vetoing its use by police and other agencies(1). Since then, several other American cities have implemented their own similar FRT bans, with Boston's city councillors(2) explicitly highlighting one particular issue: the technology's bias. |
| format | Online Article Text |
| id | pubmed-7575263 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2020 |
| publisher | Elsevier Ltd. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-75752632020-10-21 Beating the bias in facial recognition technology Lunter, Jan Biometric Technology Today Article In 2019, San Francisco became the first US city to ban facial recognition technology (FRT), specifically vetoing its use by police and other agencies(1). Since then, several other American cities have implemented their own similar FRT bans, with Boston's city councillors(2) explicitly highlighting one particular issue: the technology's bias. Elsevier Ltd. 2020-10 2020-10-20 /pmc/articles/PMC7575263/ http://dx.doi.org/10.1016/S0969-4765(20)30122-3 Text en Copyright © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| spellingShingle | Article Lunter, Jan Beating the bias in facial recognition technology |
| title | Beating the bias in facial recognition technology |
| title_full | Beating the bias in facial recognition technology |
| title_fullStr | Beating the bias in facial recognition technology |
| title_full_unstemmed | Beating the bias in facial recognition technology |
| title_short | Beating the bias in facial recognition technology |
| title_sort | beating the bias in facial recognition technology |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575263/ http://dx.doi.org/10.1016/S0969-4765(20)30122-3 |
| work_keys_str_mv | AT lunterjan beatingthebiasinfacialrecognitiontechnology |