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Published June 11, 2019 | Version pre-print
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
  • 2. Open University of Cyprus & RISE

Description

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 of- fer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and propri- etary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some of- fer more interpretation on images, they may exhibit less fair- ness toward the depicted persons, by misuse of gender-related tags and/or making judgments on a person’s physical appear- ance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarked against a ground truth.

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Additional details

Funding

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