Incorrect labels in training data cause AI models to memorize wrong patterns, distort decision boundaries, and produce systematically biased predictions. A 2021 MIT CSAIL study found an average 3.4% label error rate across 10 major ML benchmarks, including ~6% in ImageNet's validation set. Structured (instance-dependent) label errors are up to 5× more damaging than random errors, and larger models memorize noise more aggressively than smaller ones. The article covers how label noise propagates through modern AI pipelines, how to detect it using techniques like confident learning (cleanlab), cross-validation disagreement, and loss-based filtering, and how to recover via relabeling, example removal, robust loss functions, and regularization. The core takeaway: clean before you scale — halving your label error rate consistently outperforms doubling your dataset size.
