How Search Engines Reinforce Who We See as “Political”

Table of links

ABSTRACT

INTRODUCTION

SEARCH ENGINES AND WOMEN’S DESCRIPTIVE REPRESENTATION

MEASURING THE EXTENT OF ALGORITHMIC REPRESENTATION

GENERAL DISCUSSION, CONCLUSION AND REFERENCES

GENERAL DISCUSSION, CONCLUSION AND REFERENCES

What is the role of search engines in upholding views of politics as a masculine and white domain? In this article, we proposed a framework of algorithmic representation of minoritized groups that theorizes this question as a chain of four intertwined types of bias, which we then empirically tested across four empirical studies. Overall, we find consistent support of all expectations. First, search engines algorithmically underrepresent women in their output in absolute terms. Second, the extent of women’s algorithmic underrepresentation in Google image searches generally tracks with their collective descriptive representation in 84 legislative bodies across 56 countries. Third, exposure to gender, race, and intersectional bias in search engine outputs consistently distort perceptions of the political reality by decreasing the estimated presence of politicians belonging to these groups among individual search engine users. Finally, these misperceptions act as a casual conduit through which algorithmic biases not only diminish the perceived chances of winning an election for minority candidates but also result in voters feeling that their voices matter less.

Our findings have several implications for the study of representation in politics. First, our account of algorithmic representation introduces search engines as an important piece in the puzzle of persistent underrepresentation of minoritized groups in contemporary politics. The findings illustrate how widely used digital tools shape—and are shaped by—existing structural inequalities; they raise concerns about the future impacts of these tools, in particular considering the rapid adoption of AI-driven systems in different societal sectors. This combination of structural and algorithmic explanations of political bias goes with and beyond more established candidate- and voter-centered approaches (see also Dolan and Hansen 2018). On the supply-side, Juenke and Shah (2016) showed that voters can rarely “choose amongst a menu of racially or politically diverse candidates” (84). Our findings suggest that search engines may compound this dearth of minoritized candidates by rendering those who are running less visible through algorithmic filtering (see Thomsen and King 2020; O’Brien 2015). Moreover, googling politics and not finding any role models who share your gender or skin color might stymy nascent political ambition in potential future candidates (Bos et al. 2022; Fox and Lawless 2011; Kanthak and Woon 2015). On the demand-side, recent studies indicate that overt forms of voter bias are disappearing (Rohrbach et al. 2023; Schwarz and Coppock 2022; Teele et al. 2018). By conceptually broadening the notion of strategic discrimination (Bateson 2020; Green et al. 2022), we show that search engine outputs can act as the basis for voters’ assessments of politicians’ electability and thus present a more subtle and indirect form of voter bias. Empirically, our mediation analyses consistently support the notion of strategic discrimination as a causal driver of race- and gender-based prejudice in contemporary politics.

Second, we have theoretically and empirically outlined the circular logic of algorithmic bias in politics (see Savaget et al. 2019). Algorithms power AI models which are trained on data reflecting existing structural inequalities; in turn, algorithmic (mal)performance amplifies social and political realities through inherent biases in their filtering (Burrell and Fourcade 2021). All findings from our experimental conditions attest to this vicious cycle in which search engines underrepresent candidates from minoritized groups and thereby diminish their perceived electoral chances. However, this pattern reverses into a virtuous cycle in the conditions where we artificially removed bias from search engine output. Algorithmic overrepresentation of minoritized groups boosts voters’ estimations of their political presence in government. This finding extends existing work on public perceptions of political udnerrepresentation (Stauffer 2021; Dolan and Hansen 2018; Dolan and Sanbonmatsu 2009) by positioning search engines as sources of public impression formation. In line with previous research (Stauffer 2021), the evidence linking perceived inclusion of minority groups to increased feelings of external efficacy is especially noteworthy. In times of fatiguing democratic support around the world (Diamond 2015; Gavras et al. 2022; Graham and Svolik 2020), algorithmic representation could play a vital role in fostering public support of political institutions.

Third, our empirical evidence contributes to ongoing public debates and cross-disciplinary research on algorithmic fairness (Kalluri 2020; Weinberg 2022; Wong 2020). Our findings are integral for increasing societal awareness about the discriminatory potential of AI-driven systems in the context of political participation (Savaget et al. 2019; Stier et al. 2022). By delineating the consequences of exposure to algorithmic representation, we showcase that the concept of "bias in, bias out" extends beyond a catchphrase; the functioning of proprietary AI-driven systems, such the ones used by Google, should be more actively incorporated into broader discussions on algorithmic governance and injustice (Birhane 2021). Accordingly, our findings provide an empirical basis for developing new regulation for preventing risks associated with the growing adoption of AI and is thus relevant for a broad range of stakeholders, including policymakers and industry, but also civil society and human right advocacy groups.

This study comes with several limitations. The algorithm audits center on a single case of Google image search. Insightful and widely used as this specific case may be, it remains unclear how our framework of algorithmic representation transfers to other AI-curated digital spaces. Future research should extend the focus to social media platforms like TikTok that heavily rely on AI to structure content resulting in the amplification of undesirable political messages, including hate and disinformation (Weimann and Masri 2023). Another limitation arises from the use of computer vision. Though manual coding suggests good reliability, this approach is limited to a binary classification and disregards the complexity of gender identity (Scheuerman et al. 2019). For ethical and baseline-related reasons regarding the use of AI, our auditing studies also ignore the question of representation of race. Critical analysis and in-depth qualitative work could help establish a benchmark for how search engines (mis)represent non-white politicians. Much of our analysis captures dynamics on the level of political institutions and groups of candidates. A promising approach would be extending our framework to the level of individual candidates to study algorithmic representation in the context of politicians with concrete political histories and, for instance, partisan identities (Pradel 2021). Finally, our experimental evidence documents algorithmic underrepresentation decreases perceived electability. However, more work is needed to delineate downstream electoral consequences of such diminished electability, ideally using observational data from actual elections or longitudinal designs. Whereas the political landscape is only slowly becoming more inclusive towards women and non-white politicians, the algorithmic landscape has been evolving rapidly in all directions. This article captured how these two landscapes are interlocked to sustain white and masculine views of politics at present; tracking how these dynamics unfold in the future remains a major concern for the legitimacy of democratic structures and processes.

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