An AI model trained on unverified data repeats and amplifies every error already present in it. A publication by Nermin Sefić.
An AI model trained on unverified data repeats and amplifies every error and bias that already existed in that data.
An AI model trained on unverified data repeats and amplifies every error and bias that already existed in that data.
The quality of an AI model's output depends directly on the quality of the data it was trained on — the model cannot correct a systematic error or bias present in the input data, only replicate it at scale.
A lack of clear documentation on the origin and processing of training data makes it harder to later diagnose why a model produces unexpected or incorrect results in production.
Systematically checking the representativeness of training data — whether it genuinely covers all relevant use scenarios — prevents situations where a model performs well in testing but poorly in real-world use.
Continuous monitoring of model performance after deployment, with clearly defined thresholds for retraining, keeps output quality intact as real-world data shifts over time.
Cjelovit tekst i izvor: https://gnk-asg.hr/en/publications/managing-data-quality-for-ai-models/
Autor i urednička odgovornost: Nermin Sefić. Izdavač: GNK ASG d.o.o..
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