Unauthorized-Attribution Detector for AI Lab Claims

Monitor news and social media for third-party claims that invoke AI lab authority, and flag ones the labs haven’t endorsed.

Difficulty: 1-week | Stack: Python, FastAPI, PostgreSQL, NewsAPI or GDELT, Claude API (citations + structured output), Celery + Redis, Next.js

Who this is for

Communications teams at AI companies, fact-checkers, and policy analysts who need to know when advocacy groups or political actors are mis-citing lab positions — the exact problem OpenAI’s disclaimer addresses.

Build steps

1. Build an ingestion pipeline that pulls articles and social posts mentioning target company names (OpenAI, Anthropic, etc.) + policy keywords via NewsAPI/GDELT on a 15-minute cadence
2. For each article, use an LLM to extract every claim that attributes a policy position to a lab (‘OpenAI supports X’, ‘According to Anthropic…’) along with the surrounding context
3. Maintain a ‘ground truth’ store of each lab’s actual stated positions (seeded from the Policy Tracker project or manual entry) as embeddings in pgvector
4. Score each extracted claim by semantic similarity to the ground-truth store; flag items below a threshold as ‘potentially unauthorized or misrepresented’
5. Expose a dashboard showing flagged items with side-by-side original claim vs. closest official statement, plus a daily digest email for subscribed users

Risks

• News volume is high and most mentions are benign — precision will be low without aggressive filtering, leading to alert fatigue before users trust the tool
• Ground-truth coverage gaps mean the system flags legitimate paraphrases as misattributions; you need a human review queue and feedback loop built in from day one
• GDELT/NewsAPI rate limits and data freshness constraints may make real-time monitoring impractical on a solo-developer budget — batch hourly is more realistic