Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

TL;DR

This paper introduces a recursive algorithm interrogation method to analyze bias in search query suggestions by creating suggestion trees, enabling deeper bias detection especially in political person-related searches.

Contribution

It proposes a novel recursive interrogation approach and suggestion trees to uncover subliminal biases in search query suggestions beyond traditional methods.

Findings

Identified bias patterns in political person-related search suggestions.

Demonstrated the effectiveness of recursive interrogation in expanding bias detection.

Provided insights into topical group bias in search suggestions.

Abstract

Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.