Perceptions in Pixels: Analyzing Perceived Gender and Skin Tone in Real-world Image Search Results

摘要

The results returned by image search engines have the power to shape peoples' perceptions about social groups. Existing work on image search engines leverages hand-selected queries for occupations like "doctor" and "engineer" to quantify racial and gender bias in search results. We complement this work by analyzing peoples' real-world image search queries and measuring the distributions of perceived gender, skin tone, and age in their results. We collect 54,070 unique image search queries and analyze 1,481 open-ended people queries (i.e. not queries for named entities) from a representative sample of 643 US residents. For each query, we analyze the top 15 results returned on both Google and Bing Images. Analysis of real-world image search queries produces multiple insights. First, less than 5% of unique queries are open-ended people queries. Second, fashion queries are, by far, the most common category of open-ended people queries, accounting for over 30% of the total. Third, the modal skin tone on the Monk Skin Tone scale is two out of ten (the second lightest) for images from both search engines. Finally, we observe a bias against older people: eleven of our top fifteen query categories have a median age that is lower than the median age in the US.