Garbage In, Garbage Out: Data Quality and AI

An old computing saying applies perfectly to AI: garbage in, garbage out.

An AI system doesn't fix bad data. It amplifies it. If your records are incomplete, outdated, or skewed toward one kind of customer, the model's answers will be incomplete, outdated, and skewed in exactly the same way. Only faster, and with more confidence.

This is why data quality work comes before the AI project, not after it. Check whether your records are complete. Check whether they represent everyone they should. Check whether anyone has looked at them lately.

Teams love to buy the model first and clean the data later. That's backwards. The model is the last step, not the first.

Get the data right, and the AI has a chance. Skip that step, and you've just automated your mess at scale.

I explain frameworks; I don't give legal counsel.