Tech Fixx · Video
Why Bad Data Breaks AI Models: Bias, Noise & Label Errors
Watch this 3 min 31 sec tutorial from the Tech Fixx channel, published .
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From the video description
An AI model can learn the wrong lesson when its training data is inaccurate, unrepresentative or poorly labelled. This explainer shows how data problems travel through the machine-learning pipeline and appear as unreliable outputs.
You’ll learn: • Incorrect and inconsistent labels • Missing, duplicated and noisy records • Sampling and representation bias • Data leakage and misleading shortcuts • Distribution shift between training and real use • Why overall accuracy can hide failures • Practical checks before deployment
Real-world performance should be monitored after launch because data and user behaviour can change.
ICO material on AI accuracy and fairness: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/
#DataQuality #AIBias #MachineLearning