An Investigation of Biases in Web Search Engine Query Suggestions

TL;DR

This paper develops a framework to analyze biases in search engine query suggestions, focusing on politician names, revealing potential biases related to gender, party, and age across major search engines.

Contribution

It introduces a systematic, automated method for detecting biases in query suggestions and applies it to real-world data from Google, Bing, and DuckDuckGo.

Findings

Identified gender, party, and age biases in query suggestions.

Found variations in suggestion stability over time.

Demonstrated differences among search engines in bias levels.

Abstract

Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. In this study, we analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. We test our approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test our framework, we collected data from the Google, Bing, and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party, or age and with regards to the stability of the suggestions over time.