Methodology
How we measure American trust in AI — transparently and reproducibly.
How the Trust Score Works
The USA AI Trust Score is a weekly composite metric (0–100) derived from thousands of public data points across social media, news, and search. Higher scores indicate greater public trust; lower scores signal concern or distrust.
The 7 Research Agents
Reddit Crawler
Analyzes AI-related subreddits for sentiment, emotion scores, and recurring themes.
Hacker News Monitor
Tracks HN discussions about AI products, safety, and trust to capture the technical community perspective.
Brave Search Agent
Gathers search results and snippets for AI trust queries across diverse demographics.
Tavily Research Agent
Retrieves news articles, blog posts, and opinion pieces about AI trust and safety.
Google Trends Tracker
Monitors search trend volume for AI-related keywords to detect emerging concerns and interest spikes.
Sentiment Analyzer
Runs Claude AI-powered emotion and sentiment scoring on every collected data item.
Trust Score Engine
Aggregates all signals into a weekly trust score using a proprietary weighted model.
Scoring Model
Emotion Scoring: Each piece of content is scored across 6 emotions — trust, distrust, fear, excitement, curiosity, and skepticism — using Claude AI.
Sentiment Labels: Content is classified as positive, negative, neutral, or mixed, contributing to platform-level sentiment breakdowns.
Trust Score Formula: The composite trust score weights positive emotions (trust, excitement, curiosity) vs. negative emotions (distrust, fear, skepticism), normalized to a 0–100 scale.
Demographic Tagging: Content is tagged by inferred demographic group based on source community and context signals.
Confidence Level: Reported as a 0–1 value reflecting data volume and consistency. Low confidence means fewer data points were collected.
Methodology Governance
Version: 1.0
Last reviewed: April 11, 2026
Automation boundary: Data collection, emotion scoring, and weekly aggregation are automated. Public methodology and site updates are manually reviewed before publication changes.
When we update this page: We revise the methodology when scoring weights, source coverage, or demographic tagging rules materially change.
Limitations & Caveats
- • This data reflects expressed sentiment in public online spaces — not a scientific random sample.
- • Demographic attribution is inferred from community context, not self-reported.
- • AI analysis may occasionally misclassify nuanced or sarcastic content.
- • Low-volume weeks may show higher variance in trust scores.
Use This Data Responsibly
USA AI Report is designed to help businesses, researchers, and policymakers understand public trust trends, not replace formal survey research or regulated risk assessments.