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Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines
Quelle: Boratto, Ludovico; Faralli, Stefano; Marras, Mirko; Stilo, Giovanni; Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021; (Communications in Computer and Information Science, 1418), 2021. S 1-16
Inhalt: 
Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.
Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.
Schlagwörter:Repräsentation; künstliche Intelligenz; online service; artificial intelligence; information retrieval; Algorithmus; algorithm; Online-Dienst; Trend; representation; search engine; Suchmaschine; trend; information retrieval; web search; bias; artificial intelligence
SSOAR Kategorie:interaktive, elektronische Medien
Verfügbarkeit:https://doi.org/10.1007/978-3-030-78818-6_5
Dokumenttyp:Graue Literatur, Bericht