Understand how mislabels occur, why they matter, and how to detect and fix them using Visual Layer.
Issue | Impact |
---|---|
Poor training data quality | Models trained on mislabeled data tend to learn the wrong patterns, leading to lower accuracy and reliability. |
Bias and skewed outcomes | Mislabels can reinforce social, demographic, or class-based bias in prediction systems. |
Wasted resources | Training on flawed data wastes compute, time, and effort—often requiring rework. |
Downstream risk | In high-stakes environments (e.g. healthcare, AV), label errors can compromise safety or correctness. |
Loss of user trust | Consistently mislabeled content erodes user confidence in model outputs. |