Methodology
How AI Fear Index Measures Public AI Concern
AI Fear Index tracks public signals of AI-related fear, concern, anger, opposition, and distrust. The goal is to identify statistically unusual changes, not to make predictions.
What the index measures
The system looks for public material connected to AI and evaluates whether the item reflects concern, fear, anger, distrust, backlash, regulation pressure, job-loss anxiety, deepfake risk, surveillance concern, safety concern, or related opposition.
Items are scored for relevance, issue type, evidence quality, geography, and alert strength.
Baselines and spikes
Alerts are based on changes against baselines. A topic or geography becomes more important when activity is unusual compared with recent historical patterns, not merely because one article uses dramatic language.
Geography
The Company tries to identify the most specific practical geography for each signal. Some items are local, national, regional, or global. Closed, restricted, or low-coverage information environments can limit geographic confidence.
Limitations
AI Fear Index monitors public signals and source availability. It does not measure private opinion, secret government activity, unpublished company decisions, or closed-community conversations that cannot be collected responsibly.