  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.
