Conference paper Open Access

Social Cues, Social Biases: Stereotypes in Annotations on People Images

Jahna Otterbacher

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  <identifier identifierType="DOI">10.5281/zenodo.2670019</identifier>
      <creatorName>Jahna Otterbacher</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-7655-7118</nameIdentifier>
      <affiliation>Faculty of Pure and Applied Sciences, Open University of Cyprus and Research Centre on Interactive Media Smart Systems and Emerging Technologies Nicosia, CYPRUS</affiliation>
    <title>Social Cues, Social Biases: Stereotypes in Annotations on People Images</title>
    <subject>linguistic biases</subject>
    <subject>social stereotypes</subject>
    <subject>social cues</subject>
    <subject>social biases</subject>
    <date dateType="Issued">2018-07-08</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2670018</relatedIdentifier>
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  <version>Accepted pre-print</version>
    <rights rightsURI="">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Human computation is often subject to systematic biases. We consider the case of linguistic biases and their consequences&lt;br&gt;
for the words that crowd workers use to describe people images in an annotation task. Social psychologists explain that when describing others, the subconscious perpetuation of stereotypes is inevitable, as we describe stereotype-congruent people and/or in-group members more abstractly than others. In an MTurk experiment we show evidence of these biases, which are exacerbated when an image&amp;rsquo;s &amp;ldquo;popular tags&amp;rdquo; are displayed, a common feature used to provide social information to workers. Underscoring recent calls for a deeper examination of the role of training data quality in algorithmic biases, results suggest that it is rather easy to sway human judgment.&lt;/p&gt;</description>
    <description descriptionType="Other">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

Copyright © 2018, Association for the Advancement of Artificial Intelligence</description>
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      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/739578/">739578</awardNumber>
      <awardTitle>Research Center on Interactive Media, Smart System and Emerging Technologies</awardTitle>
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