Conference paper Open Access

Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images

Kyriakou Kyriakos; Barlas Pınar; Kleanthous Styliani; Otterbacher Jahna


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    "title": "Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images", 
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    "notes": "This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and under Grant Agreement No 810105 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.", 
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    "publication_date": "2019-10-30", 
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        "name": "Kyriakou Kyriakos"
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        "orcid": "0000-0002-7882-7927", 
        "affiliation": "Research Centre on Interactive Media, Smart Systems and Emerging Technologies (Nicosia, CYPRUS)", 
        "name": "Barlas P\u0131nar"
      }, 
      {
        "orcid": "0000-0003-1594-1340", 
        "affiliation": "Research Centre on Interactive Media, Smart Systems and Emerging Technologies (Nicosia, CYPRUS) and 2Cyprus Center for Algorithmic Transparency, Open University of Cyprus (Latsia, CYPRUS)", 
        "name": "Kleanthous Styliani"
      }, 
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        "affiliation": "Research Centre on Interactive Media, Smart Systems and Emerging Technologies (Nicosia, CYPRUS) and 2Cyprus Center for Algorithmic Transparency, Open University of Cyprus (Latsia, CYPRUS)", 
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    "description": "<p>There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of<br>\nimage tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information<br>\necosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related<br>\ntags and/or making judgments on a person&rsquo;s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarkedagainst a ground truth.</p>"
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