To "See" is to Stereotype

نویسندگان

چکیده

Machine-learned computer vision algorithms for tagging images are increasingly used by developers and researchers, having become popularized as easy-to-use "cognitive services." Yet these tools struggle with gender recognition, particularly when processing of women, people color non-binary individuals. Socio-technical researchers have cited data bias a key problem; training datasets often over-represent contexts that convey social stereotypes. The psychology literature explains learn stereotypes, in part, observing others particular roles contexts, can inadvertently to associate scenes, occupations activities. Thus, we study the extent which image mimic this phenomenon. We design controlled experiment, examine interdependence between algorithmic recognition context depicted person's gender. In spirit auditing understand machine behaviors, create highly dataset images, imposed on gender-stereotyped backgrounds. Our methodology is reproducible our code publicly available. Evaluating five proprietary algorithms, find three, inference hindered background introduced. Of two "see" both backgrounds gender, it one whose output most consistent human stereotyping processes superior recognizing discuss accuracy--fairness trade-off, well importance black boxes better understanding double-edged sword.

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ژورنال

عنوان ژورنال: Proceedings of the ACM on human-computer interaction

سال: 2021

ISSN: ['2573-0142']

DOI: https://doi.org/10.1145/3432931