{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T12:45:24Z","timestamp":1754397924413,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685229"}],"license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,5]]},"abstract":"<jats:p>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 \u2014 bias introduced by individuals who label datasets \u2014 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.<\/jats:p>","DOI":"10.3233\/faia240191","type":"book-chapter","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T14:52:17Z","timestamp":1717685537000},"source":"Crossref","is-referenced-by-count":3,"title":["Investigating Labeler Bias in Face Annotation for Machine Learning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-2453","authenticated-orcid":false,"given":"Luke","family":"Haliburton","sequence":"first","affiliation":[{"name":"LMU Munich, Germany"},{"name":"Munich Center for Machine Learning (MCML), Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2874-2909","authenticated-orcid":false,"given":"Sinksar","family":"Ghebremedhin","sequence":"additional","affiliation":[{"name":"LMU Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-7890","authenticated-orcid":false,"given":"Robin","family":"Welsch","sequence":"additional","affiliation":[{"name":"Aalto University, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1990","authenticated-orcid":false,"given":"Albrecht","family":"Schmidt","sequence":"additional","affiliation":[{"name":"LMU Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5462-8782","authenticated-orcid":false,"given":"Sven","family":"Mayer","sequence":"additional","affiliation":[{"name":"LMU Munich, Germany"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","HHAI 2024: Hybrid Human AI Systems for the Social Good"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T13:53:48Z","timestamp":1718373228000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,5]]},"ISBN":["9781643685229"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240191","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,6,5]]}}}