Published October 30, 2019 | Version Published
Conference paper Open

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

  • 1. Research Centre on Interactive Media, Smart Systems and Emerging Technologies (Nicosia, CYPRUS)
  • 2. Research Centre on Interactive Media, Smart Systems and Emerging Technologies (Nicosia, CYPRUS) and 2Cyprus Center for Algorithmic Transparency, Open University of Cyprus (Latsia, CYPRUS)


There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of
image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information
ecosystem, 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
tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarkedagainst a ground truth.


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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RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission
CyCAT – Cyprus Center for Algorithmic Transparency 810105
European Commission