How to Recognize Labeling Errors and Ask for Corrections in Machine Learning Datasets

When you're building a machine learning model, the quality of your data matters more than the complexity of your algorithm. A state-of-the-art neural network won't fix bad labels. In fact, labeling errors are one of the biggest hidden killers of model accuracy - often worse than using the wrong architecture or insufficient training data.

Studies show that even high-quality datasets like ImageNet contain around 5.8% labeling errors. In real-world applications - think medical imaging, autonomous vehicles, or drug discovery - those errors aren’t just numbers. They’re missed tumors, undetected pedestrians, or misclassified chemicals. And if your model learns from those mistakes, it will repeat them. That’s why recognizing and correcting labeling errors isn’t optional. It’s essential.

What Labeling Errors Actually Look Like

Labeling errors aren’t always obvious. You won’t always see a mislabeled cat as a dog. More often, they’re subtle, systematic, and easy to miss. Here are the most common types you’ll run into:

• Missing labels: An object is in the image or text, but no one annotated it. In autonomous driving datasets, this means a pedestrian wasn’t boxed. In medical reports, it means a symptom wasn’t tagged.
• Incorrect fit: The bounding box is too big, too small, or off-center. A tumor might be labeled with a box that includes healthy tissue - confusing the model about what to look for.
• Wrong class: A label is applied to the wrong category. A benign growth labeled as malignant. A drug name misclassified as a side effect.
• Boundary errors: Especially in text, entity boundaries are wrong. "Metformin 500mg" might be labeled as one drug when it’s two separate entities: the drug and the dosage.
• Ambiguous examples: A photo could reasonably belong to two classes. A blurry image of a cat and dog. A medical note with conflicting symptoms. These aren’t always errors - but they need human review.
• Out-of-distribution samples: Data that doesn’t fit any class at all. A photo of a tree in a dataset of hospital equipment. A handwritten note in a dataset of typed clinical records.

According to MIT’s Data-Centric AI research, 41% of labeling errors in entity recognition involve wrong boundaries, and 33% involve misclassified types. These aren’t random typos. They’re systemic issues - often caused by unclear instructions, rushed annotation, or changing guidelines mid-project.

How to Spot These Errors Without a PhD

You don’t need to be a data scientist to find these problems. Here are three practical, real-world ways to catch labeling errors:

1. Use Algorithmic Tools Like cleanlab

cleanlab is an open-source tool built on confident learning - a method developed at MIT and Harvard. It doesn’t need you to write code. You just feed it your dataset and your model’s predictions. It then flags examples where the model is confidently wrong - meaning the label is likely incorrect.

It works across text, images, and tabular data. In one case, a pharmaceutical company used cleanlab to scan 12,000 drug interaction notes. The tool flagged 892 potential errors. After human review, 71% were confirmed as real mistakes - mostly mislabeled drug names and incorrect interaction types.

cleanlab’s strength? It finds errors you didn’t know existed. Its precision rate is 65-82%, meaning most of the flagged examples are real problems - not false alarms.

2. Run Multi-Annotator Consensus

Have three people label the same sample. If two agree and one disagrees, the outlier is worth checking. This isn’t just theory - Label Studio’s analysis of 1,200 annotation projects showed this method cuts error rates by 63%.

It’s slower and costs more, but in high-stakes areas like radiology or clinical trials, it’s worth it. One hospital reduced diagnostic labeling errors by 54% after switching to triple-annotated datasets for AI-assisted tumor detection.

3. Use Model-Assisted Validation

Train a simple model on your labeled data, then run it back over the same dataset. Look for cases where the model is highly confident but wrong. For example, if your model says with 98% certainty that a label is "Type 2 Diabetes," but the original text says "Type 1," that’s a red flag.

Encord’s Active tool does this automatically. It works best when your baseline model has at least 75% accuracy. In a 2023 medical imaging project, this method caught 85% of labeling errors - including many that were missed by human annotators.

How to Ask for Corrections Without Creating Conflict

Finding an error is only half the battle. The harder part is getting it fixed - especially when you’re not the original annotator. Here’s how to do it professionally:

• Don’t say: "This is wrong." Say: "I noticed this example might need a second look. The label says ‘hypertension,’ but the note mentions ‘normal BP.’ Could we confirm?"
• Provide context. Attach the original text or image. Include the model’s prediction confidence if you used one. This turns a criticism into a collaborative review.
• Use tools that track changes. Platforms like Argilla and Datasaur let you flag errors and assign them to annotators. Use those features. Don’t email screenshots.
• Don’t assume malice. Most errors come from ambiguous instructions, not carelessness. If multiple people made the same mistake, the problem is your guidelines - not your team.

TEKLYNX found that 68% of labeling errors stem from unclear instructions. So when you ask for a correction, also ask: "What part of the guidelines was confusing?" Fix the rule, not just the label.

Tools That Actually Work (And Which to Choose)

Not all tools are created equal. Here’s what works now, based on real-world use:

For most teams, start with Argilla if you’re working with text. Use Datasaur if you’re already annotating in their platform. Use cleanlab if you have technical staff and need maximum accuracy. Avoid tools that don’t integrate with your existing workflow - they’ll just create more work.

What Happens If You Ignore Labeling Errors

Ignoring labeling errors is like building a house on cracked concrete. The walls might look fine at first. But over time, everything shifts.

MIT’s Professor Aleksander Madry says: "Label errors create a fundamental limit on model performance. No amount of model complexity can overcome them."

Companies that skip error correction see 20-30% lower model accuracy than those that don’t, according to Gartner. In healthcare, that means misdiagnoses. In finance, it means fraudulent transactions slipping through. In logistics, it means wrong packages shipped.

One major pharmacy chain used an AI system to flag high-risk prescriptions. But because 12% of the training data had wrong drug labels, the system flagged 37% of safe prescriptions as dangerous. Pharmacists started ignoring it. The tool was scrapped. All because no one checked the labels.

How to Build a Sustainable Correction Process

Fixing errors once isn’t enough. You need a system. Here’s a simple workflow that works:

1. Load your dataset into a tool like Argilla or cleanlab. (1-2 hours)
2. Run error detection. Let the tool flag suspicious examples. (5-30 minutes)
3. Review flagged items with a domain expert - not just an annotator. (2-5 hours per 1,000 errors)
4. Update your labeling guidelines based on what you find. (1-2 hours)
5. Retrain your model with the corrected data. (1-24 hours)

Do this every time you update your dataset. Make it part of your MLOps pipeline - not a one-time cleanup.

And remember: the goal isn’t zero errors. That’s impossible. The goal is to keep errors below 3%. In most cases, that’s enough to dramatically improve model reliability.

What’s Coming Next

Label error detection is evolving fast. By 2026, every enterprise annotation platform will include it as a standard feature. New tools are coming that can detect errors in multimodal data - like images with text captions - which is currently a weak spot.

MIT is testing "error-aware active learning," which prioritizes labeling the data most likely to contain errors. Early results show it cuts correction time by 25%.

But the biggest shift? The mindset. We’re moving from "data is just input" to "data is the model." If you treat your labels like a static file, you’re behind. Treat them like living, evolving truth - and your models will thank you.