How Data Quality Constrains AI Model Outputs

AI in this project is used to organize information, extract features, and run a first screen. Those steps depend on the records they receive. A missing field, a revised print, or a timestamp that does not match the event it describes will show up again in the output.

The technical workflow flags gaps and conflicts before a researcher reads a screened list. Flagging a record does not repair it. It stops that record from being treated as complete. Where history and the live record disagree, the disagreement stays visible for review.

Algorithmic output is reference material. Key conclusions still go through human cross-review. Automating the sorting step does not move the judgment step off the team.

This note shares an internal research perspective. It does not describe a trading system or promise a research result.