Input and training data errors

AI errors often stem from problems in training data or user input. When models learn from flawed data, they confidently repeat those flaws for every user. Input errors happen when systems can't understand what users really mean.

Consider training data problems first. A voice assistant trained mostly on American English fails consistently for British users. Every British person experiences identical recognition failures. These aren't random bugs but systematic blind spots.

Mislabeled training data causes especially confusing mistakes. If training data mixed up muffins and cupcakes, the AI now confidently misidentifies every muffin. Users see obvious errors and wonder how advanced AI fails at simple tasks. They don't know the AI learned these mistakes from its training.

Input errors frustrate differently. Users expect AI to understand typos and context. Searching for "resturant near me" should obviously return restaurant results. When AI processes this literally and returns nothing, users feel the system is being deliberately obtuse. They expect intelligence, not just pattern matching. Both error types require different solutions. Training data problems need new data collection and model retraining. Input errors need better preprocessing and understanding of user intent.