publication . Conference object . Other literature type . 2019

What Makes an Image Tagger Fair? Proprietary Auto-tagging and Interpretations on People Images

Pinar, Barlas; Styliani, Kleanthous; Kyriakou Kyriakos; Jahna, Otterbacher;
Open Access English
  • Published: 30 Oct 2019
  • Publisher: ACM
Abstract
Image analysis algorithms have been a boon to personalization in digital systems and are now widely available via easy-to-use APIs. However, it is important to ensure that they behave fairly in applications that involve processing images of people, such as dating apps. We conduct an experiment to shed light on the factors influencing the perception of “fairness." Participants are shown a photo along with two descriptions (human- and algorithm-generated). They are then asked to indicate which is “more fair" in the context of a dating site, and explain their reasoning. We vary a number of factors, including the gender, race and attractiveness of the person in the ...
Subjects
free text keywords: algorithmic bias, computer vision, fairness, image analysis
Funded by
EC| RISE
Project
RISE
Research Center on Interactive Media, Smart System and Emerging Technologies
  • Funder: European Commission (EC)
  • Project Code: 739578
  • Funding stream: H2020 | SGA-CSA
,
EC| CyCAT
Project
CyCAT
Cyprus Center for Algorithmic Transparency
  • Funder: European Commission (EC)
  • Project Code: 810105
  • Funding stream: H2020 | CSA
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Conference object . 2019
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Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
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publication . Conference object . Other literature type . 2019

What Makes an Image Tagger Fair? Proprietary Auto-tagging and Interpretations on People Images

Pinar, Barlas; Styliani, Kleanthous; Kyriakou Kyriakos; Jahna, Otterbacher;