Examining Racial Stereotypes in YouTube Autocomplete Suggestions

TL;DR

This study conducts a systematic audit of YouTube search autocomplete suggestions for race-related queries among Whites, Blacks, Asians, and Hispanics. Using a combination of algorithmic-output auditing and critical discourse analysis, the authors identify five sociocultural contexts—Appearance, Ability, Culture, Social Equity, and Manner—under which autocomplete suggestions embed and propagate racial biases. The findings reveal ongoing historical stereotypes, instances of cultural appropriation, and intergroup tensions, alongside counter-narratives that challenge bias. The work argues for proactive content-moderation reforms and greater transparency in autocomplete sourcing to mitigate representational harms on YouTube’s platform and similar search environments.

Abstract

Autocomplete is a popular search feature that predicts queries based on user input and guides users to a set of potentially relevant suggestions. In this study, we examine what YouTube autocompletes suggest to users seeking information about race on the platform. Specifically, we perform an algorithm output audit of autocomplete suggestions for input queries about four racial groups and examine the stereotypes they embody. Using critical discourse analysis, we identify five major sociocultural contexts in which racial information appears -Appearance, Ability, Culture, Social Equity, and Manner. We found that the participatory nature of YouTube produces a multifaceted representation of race-related content in its search outputs, characterized by enduring historical biases, aggregated discrimination, and interracial tensions, while simultaneously depicting minority resistance and aspirations of a post-racial society. We call for innovations in content moderation policy design and enforcement to address existing racial harms in YouTube search outputs.

Figures (10)

• Figure 1: An example of YouTube's autocomplete function in action. Typing any keywords (e.g., "white") triggers a list of query options that users may select.
• Figure 1: Examples of autocomplete results for the racial category "White."
• Figure 2: Screenshot of a YouTube search for one of our input queries. It shows five of the autocomplete results we collected that correspond to the query.
• Figure 3: A YouTube video suggested by the autocomplete “how often should a black man wash his hair.”
• Figure 4: A YouTube video suggested by the autocomplete "black woman can't buy a dress."