Investigating Labeler Bias in Face Annotation for Machine Learning Article Swipe
Luke Haliburton
,
Sinksar Ghebremedhin
,
Robin Welsch
,
Albrecht Schmidt
,
Sven Mayer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia240191
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia240191
In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence. One key under-explored challenge is labeler bias — bias introduced by individuals who label datasets — which can create inherently biased datasets for training and subsequently lead to inaccurate or unfair decisions in healthcare, employment, education, and law enforcement. Hence, we conducted a study (N=98) to investigate and measure the existence of labeler bias using images of people from different ethnicities and sexes in a labeling task. Our results show that participants hold stereotypes that influence their decision-making process and that labeler demographics impact assigned labels. We also discuss how labeler bias influences datasets and, subsequently, the models trained on them. Overall, a high degree of transparency must be maintained throughout the entire artificial intelligence training process to identify and correct biases in the data as early as possible.
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.3233/faia240191
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA240191
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- hybrid
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.3233/faia240191Digital Object Identifier
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Investigating Labeler Bias in Face Annotation for Machine LearningWork title
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book-chapterOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-06-05Full publication date if available
- Authors
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Luke Haliburton, Sinksar Ghebremedhin, Robin Welsch, Albrecht Schmidt, Sven MayerList of authors in order
- Landing page
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https://doi.org/10.3233/faia240191Publisher landing page
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA240191Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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Annotation, Face (sociological concept), Computer science, Artificial intelligence, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1963947469, https://openalex.org/W3029264758, https://openalex.org/W2783668259, https://openalex.org/W3160665248, https://openalex.org/W1577841485, https://openalex.org/W2941486823, https://openalex.org/W2032484416, https://openalex.org/W1544543982, https://openalex.org/W2475282416, https://openalex.org/W2594940643, https://openalex.org/W4225378656, https://openalex.org/W3044248538, https://openalex.org/W4281568480, https://openalex.org/W4309618884, https://openalex.org/W1973485875, https://openalex.org/W2783946743, https://openalex.org/W2955870353, https://openalex.org/W3133702157, https://openalex.org/W3120485916, https://openalex.org/W2251311344, https://openalex.org/W2941985495, https://openalex.org/W4287125557, https://openalex.org/W2970850777, https://openalex.org/W3093541323, https://openalex.org/W3129794348, https://openalex.org/W2150690631, https://openalex.org/W4289328212, https://openalex.org/W2062526841, https://openalex.org/W1507645993, https://openalex.org/W4288258593, https://openalex.org/W2941070884, https://openalex.org/W4307182327, https://openalex.org/W4220699706, https://openalex.org/W2109453861, https://openalex.org/W2791423782, https://openalex.org/W4288327876, https://openalex.org/W4211022696, https://openalex.org/W2407816088, https://openalex.org/W2013607934, https://openalex.org/W3003385079, https://openalex.org/W3092124192, https://openalex.org/W4283168736, https://openalex.org/W3148183269 |
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