Defending Open Data with Intelligence
summary
An open-source scanner polls Open Data Hub APIs, runs daily automated scans and validates fields against typed rules, uniqueness and integrity checks, then surfaces results in a dashboard. It adds LLM-powered semantic checks for image/description alignment and mistranslations, uses a hybrid design to restrain LLM drift, and plans ML outlier detection plus synthetic-data stress tests. The system already flags recurring quality regressions over time and requires human-in-the-loop verification before issuing fixes.
AI-written summary · about
chapters (6)
- 0:00Introduction and team
- 1:49Importance of data quality
- 4:20Data validation solution
- 8:46AI and LLM integration
- 12:26Ethics and human oversight
- 13:10Q&A session
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