Report · AI & Tech

Trust in AI to fight online misinformation remains low

Reading

In a Verasight survey of 1,000 U.S. adults conducted Nov. 14 to 20, 2025, 67% of Americans said they had little or no trust in AI to identify and prevent the spread of misinformation online. This includes 34% who said they trust it not much and 32% who said they trust it not at all.

About three-in-ten said they trusted AI somewhat or a great deal to combat misinformation (28%), with 23% who said somewhat and 6% who said a great deal.

Topline

response scale

Topline scale

67% of Americans say they have little or no trust in AI to combat online misinformation.

How much do you trust artificial intelligence (AI) to identify and prevent the spread of misinformation online?

  • Not much 34.2%
  • Not at all 32.3%
  • Somewhat 22.5%
  • Not sure 5.5%
  • A great deal 5.5%

2025 · base n 1,000 · +/- 3.2%

Module 3

View source

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Field dates
2025-11-14 → 2025-11-20
Base (unweighted)
1,000
Margin of error
+/- 3.2%
Module
Module 3

Source

Citation

Verasight APHA Omnibus Survey #2025-148, fielded November 14-20, 2025, N=1,000 US adults age 18+, +/- 3.2%.

https://reports.verasight.io/r/verasight-apha-2025

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. When a wave report is published, its field dates, sample sizes, and module breakdown are listed in that report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
The canonical report hub is reports.verasight.io. Verified legacy reports remain on their original hosts, and unpublished reports are marked pending.
Transparency
Verasight is a member of the American Association for Public Opinion Research Transparency Initiative.

The canonical report hub is reports.verasight.io; older reports may remain on verified legacy hosts.