Conference paper Open Access

Investigating User Perception of Gender Bias in Image Search

Jahna Otterbacher; Alessandro Checco; Gianluca Demartini; Paul Clough


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      "title": "SIGIR '18- The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval"
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    "doi": "10.1145/3209978.3210094", 
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    "language": "eng", 
    "title": "Investigating User Perception of Gender Bias in Image Search", 
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    "notes": "This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE \u2013 Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2)  and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.\n\n\u00a9 Author. \u00a9 ACM 2018. This is the accepted version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of SIGIR 2018, DOI: https://doi.org/10.1145/3209978.3210094, Jahna Otterbacher, Alessandro Checco, Gianluca Demartini, and Paul Clough, \"Investigating user perception of gender bias in image search: the role of sexism\".In SIGIR '18- The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.   https://www.acm.org/publications/policies/copyright-policy", 
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      "Jahna Otterbacher, Jo Bates, and Paul Clough. 2017. Competent Men and Warm Women: Gender Stereotypes and Backlash in Image Search Results. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 6620\u20136631. https://doi.org/10.1145/3025453.3025727", 
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    ], 
    "keywords": [
      "Gender stereotypes", 
      "Search engine bias", 
      "User perceptions"
    ], 
    "publication_date": "2018-07-01", 
    "creators": [
      {
        "orcid": "0000-0002-7655-7118", 
        "affiliation": "Open University of Cyprus", 
        "name": "Jahna Otterbacher"
      }, 
      {
        "orcid": "0000-0002-0981-3409", 
        "affiliation": "University of Sheffield", 
        "name": "Alessandro Checco"
      }, 
      {
        "orcid": "0000-0002-7311-3693", 
        "affiliation": "University of Queensland", 
        "name": "Gianluca Demartini"
      }, 
      {
        "affiliation": "University of Sheffield", 
        "name": "Paul Clough"
      }
    ], 
    "meeting": {
      "acronym": "ACM SIGIR 2018", 
      "url": "http://sigir.org/sigir2018/", 
      "dates": "8-12 July 2018", 
      "place": "Ann Arbor Michigan, U.S.A.", 
      "title": "The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval"
    }, 
    "access_right": "open", 
    "resource_type": {
      "subtype": "conferencepaper", 
      "type": "publication", 
      "title": "Conference paper"
    }, 
    "description": "<p>There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.</p>"
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