Measuring Search Engine Bias in European Women's Image Results using Machine Learning Algorithms

Autor: Pisker, Barbara, Đokić, Kristian, Martinović, Marko
Přispěvatelé: Endrit, Xhina, Klesti, Hoxha
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Popis: This paper focuses on the issue of image search engine results, which many authors claim are the result of biases, thereby multiplying those same biases. The Google search engine was analysed, where images of women from nine countries of the European Union were searched, but using three different languages to generate queries. In this way, we tried to compare the prejudices of other language groups reflected in the results obtained using the search engine. Two thousand seven hundred images of women were collected, and to quantify the results, an artificial intelligence algorithm was used to calculate the probability of nudity in the image. The hypothesis that there is no difference between the perception of women for a particular country by English, Chinese and Russian language users was generally rejected because there are statistically significant differences in 6 out of 9 countries.
Databáze: OpenAIRE