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

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.