Finding the white male: The prevalence and consequences of algorithmic gender and race bias in political Google searches

TL;DR

This paper investigates how Google’s political image searches reflect and reinforce racial and gender biases, leading to distorted perceptions of political reality and highlighting issues of algorithmic fairness and injustice.

Contribution

It introduces a framework for analyzing algorithmic bias in political search results and demonstrates how these biases perpetuate structural inequalities and influence political perceptions.

Findings

Search results underrepresent women and non-white politicians.

Biases in search results distort perceptions of political reality.

Algorithmic biases reinforce white and masculine dominance in politics.

Abstract

Search engines like Google have become major information gatekeepers that use artificial intelligence (AI) to determine who and what voters find when searching for political information. This article proposes and tests a framework of algorithmic representation of minoritized groups in a series of four studies. First, two algorithm audits of political image searches delineate how search engines reflect and uphold structural inequalities by under- and misrepresenting women and non-white politicians. Second, two online experiments show that these biases in algorithmic representation in turn distort perceptions of the political reality and actively reinforce a white and masculinized view of politics. Together, the results have substantive implications for the scientific understanding of how AI technology amplifies biases in political perceptions and decision-making. The article contributes to ongoing public debates and cross-disciplinary research on algorithmic fairness and injustice.