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AI Adoption Among Black Americans

Black Americans are among the most active adopters of AI, especially among young people — and that adoption coexists with well-documented, well-founded concerns about algorithmic fairness. Pew Research Center data shows 35% of Black teens use AI chatbots daily, the highest rate of any racial or ethnic group measured, with about 71% using chatbots at least sometimes. Black adults report robust everyday use of generative AI for search, writing, and learning. At the same time, only about 19% of U.S. adults say AI designers take Black perspectives into account well, and documented issues — from facial-recognition accuracy on darker skin tones to biased model outputs — give that caution a concrete basis. This page presents the adoption patterns, tool preferences, adult everyday use, and the representation and trust barriers — factually and in context.

4 visualizations 3 sources Last updated June 2026 Free to embed
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Chart 1 · Youth adoption

Daily AI chatbot use among teens, by race/ethnicity

Teen AI chatbot use by race/ethnicity (Pew 2025)

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Source: Pew Research Center, “Teens, Social Media and AI Chatbots 2025” (1,458 U.S. teens): 35% of Black teens use AI chatbots daily — the highest of any group measured — compared with 33% of Hispanic and 22% of White teens. About 71% of Black teens use chatbots at least sometimes.
Chart 2 · Tool preferences

AI tools Black teens use more

Tool mix: Black teens use Gemini & Meta AI more

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Source: Pew 2025: ChatGPT leads across all groups, but Black teens are more likely than White peers to use Gemini and Meta AI, consistent with a mobile-first, platform-diverse usage pattern. Directional shares from Pew’s teen survey.
Chart 3 · Adults & everyday use

Black adults’ AI use in daily life

Black adults’ everyday generative-AI use (directional)

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Source: Brookings / NORC nationwide survey 2026: in a nationally representative survey with race categories, Black adults report robust generative-AI use for personal purposes, in line with or above the overall 57% personal-use rate, with high engagement in search, writing help, and learning. Exact subgroup rates vary by survey and sample size.
CHART 4 · BARRIERS & TRUST

Representation and algorithmic-bias concerns

Representation gap: who AI is seen to design for

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Source: Pew 2026: only ~19% of U.S. adults say AI designers consider Black perspectives well; documented algorithmic-bias concerns (e.g. facial-recognition accuracy on darker skin tones, biased outputs) shape trust. High adoption coexists with well-founded caution about fairness.

About this data

This page presents AI adoption among Black Americans using race/ethnicity breakdowns from Pew Research Center’s 2025 teen survey and 2026 AI analysis, Brookings’ NORC-based nationwide survey, and academic research on algorithmic bias. We present these patterns factually and in context, without drawing evaluative conclusions about any group.

The AI Behavior Index is the research arm of OneChat AI, an integrated multi-model AI platform. We compile and analyze data from primary research sources to make AI adoption and market trends more accessible to journalists, researchers, and decision-makers.

A note on data scope and care: the cleanest race/ethnicity adoption breakdowns currently available are for teens (Pew 2025), so several headline figures describe teens specifically, not all Black adults — we label this throughout. Adult subgroup rates come from nationally representative surveys but have larger uncertainty at the subgroup level due to smaller sample sizes. The algorithmic-bias concerns are documented in peer-reviewed and journalistic sources and are presented as factual context, not opinion.

Methodology notes: teen figures are from Pew’s probability-based survey of 1,458 U.S. teens; adult figures are from Brookings’ NORC nationwide survey and Pew adult surveys, with wider margins of error at the subgroup level. Tool-preference figures are directional. The representation figure (~19%) is from a Pew adult survey. “Black Americans” is a broad category spanning many backgrounds and generations; aggregate figures smooth over that diversity. Where data is teen-specific, we say so.

Sources used on this page:

Corrections or suggestions: research@aibehaviorindex.org

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Every statistic shown is sourced from a publicly available study, survey, or report. We aggregate, organize, and contextualize this data — but the underlying research is conducted by the cited sources. Click any source link to access the original methodology. If you run into any issues or have a study to suggest, contact us at research@aibehaviorindex.org.