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

When you're training a machine learning model, the quality of your data matters more than the model itself. A state-of-the-art neural network can’t fix bad labels. Studies show that even top-tier datasets like ImageNet contain around 5.8% labeling errors - and commercial datasets often have between 3% and 15%. These aren’t just typos. They’re misclassified images, missing annotations, or wrong boundaries around objects. If you’re working in healthcare, autonomous systems, or diagnostics, these errors can mean life-or-death consequences.

What Labeling Errors Actually Look Like

Labeling errors aren’t random. They follow patterns. In object detection tasks - like identifying tumors in X-rays or pedestrians in self-driving car footage - the most common errors are:

Missing labels (32%): An object is present but not annotated at all. A cancerous nodule in a lung scan that was overlooked.
Incorrect fit (27%): The bounding box is too big, too small, or misaligned. A tumor labeled as a lymph node because the labeler misjudged the shape.
Midstream tag additions (21%): The annotation rules changed halfway through the project, and old data wasn’t updated. One team labeled "benign" and "malignant," then added "indeterminate" later - but never went back to fix old labels.
Wrong entity boundaries (41%): Common in text data. A drug name like "Lisinopril-HCTZ" is labeled as one entity when it’s actually two separate drugs.
Misclassified types (33%): A "diabetes" diagnosis is tagged as "hypertension" because the labeler confused similar conditions.

These aren’t just "mistakes." They’re systemic failures - often caused by unclear instructions. TEKLYNX’s analysis of 500 industrial labeling projects found that 68% of errors came from vague or ambiguous guidelines. If your annotators don’t know exactly what to look for, they’ll guess. And guesses become errors.

How to Spot Them - Tools and Methods

You can’t catch every error by eye. Even the most careful human will miss patterns. That’s why tools like cleanlab is an open-source framework that uses confident learning to detect label noise by analyzing model predictions and ground truth labels are now essential. Here’s how the main methods work:

Algorithmic detection (cleanlab): It looks for examples where the model is highly confident the label is wrong. For example, if a model predicts a 95% chance that an image is a benign tumor, but it’s labeled malignant, cleanlab flags it. It finds 78-92% of errors with 65-82% precision. Works best with datasets over 1,000 samples.
Multi-annotator consensus: Have three people label the same image or text. If two agree and one doesn’t, the outlier gets flagged. This reduces errors by 63%, but it triples your labeling cost. Best for high-stakes domains like radiology.
Model-assisted validation (Encord Active): Train a simple model on your labeled data, then run it again. If the model predicts something wildly different from the label - and it’s confident - that’s a red flag. This catches 85% of errors when the model has at least 75% baseline accuracy.

Each tool has limits. cleanlab struggles with class imbalance - if one label appears 10 times more than another, it might flag rare but valid cases as errors. Datasaur’s error detection only works for text and tabular data, not images. Argilla is great for text classification but breaks down with more than 20 labels. Encord needs 16GB+ RAM for datasets over 10,000 images. Choose based on your data type and team size.

How to Ask for Corrections - Without Causing Chaos

Finding errors is half the battle. Fixing them without disrupting your workflow is the other half. Here’s how to do it right:

Don’t just send a list. Create a prioritized list: flag the highest-confidence errors first. Cleanlab ranks them by likelihood of being wrong - start there.
Provide context. Don’t say: "This label is wrong." Say: "This X-ray shows a 7mm nodule in the right upper lobe. The model predicts 94% benign. The current label is malignant. Review the original scan and confirm." Include the image, model prediction, and confidence score.
Use version-controlled guidelines. If your team keeps changing what counts as a "tumor," you’ll keep making the same errors. Document rules in a shared wiki. Link to examples. Update only when necessary - and archive old versions.
Require double-checks. For every flagged error, have a second annotator verify the correction. Label Studio’s data shows this increases accuracy from 65% to 89%.
Track changes. Use audit logs. If you fix 120 labels and accuracy drops, you need to know who changed what and why. TEKLYNX found that teams with audit trails resolved root causes 4x faster.

One team at a major hospital used this process on a 5,000-image radiology dataset. They found 12.7% labeling errors. After corrections, errors dropped to 2.3%. But it took 180 person-hours. The payoff? Their diagnostic model’s accuracy jumped from 81% to 89%. That’s not just a number - it’s fewer missed cancers.

Three tiny annotators arguing over how to split a drug name label correctly.

What Experts Say - And What You Should Worry About

Curtis Northcutt, creator of cleanlab, says: "Fixing just 5% of label errors in CIFAR-10 improved model accuracy by 1.8%. That’s more than most architectural tweaks achieve." MIT’s Professor Aleksander Madry goes further: "No amount of model complexity can overcome bad labels." But there’s a catch. Dr. Rachel Thomas from USF warns: "Algorithms don’t understand context. They might flag a rare disease as an error - because it’s rare - even though it’s correct." If you blindly accept every algorithm suggestion, you risk erasing minority classes. Always involve domain experts. A radiologist should review flagged lung nodules. A pharmacist should check drug name labels.

Industry adoption is growing fast. In 2020, only 32% of enterprises had formal label error detection. Now, 78% do. The FDA’s 2023 guidance on AI medical devices now requires it. If you’re building anything for healthcare, you’re not just optimizing - you’re complying.

Where This Is Headed

The next wave is automation. Cleanlab’s 2024 update will include medical imaging-specific error detection - because medical data has 38% more errors than general images. Argilla is integrating with Snorkel to let users write rules like: "If drug name contains ‘-HCTZ,’ always split into two entities." MIT is testing "error-aware active learning," where the system asks humans to label only the examples most likely to be wrong - cutting correction time by 25%.

By 2026, Gartner predicts every enterprise annotation tool will include built-in error detection. Right now, the biggest problem isn’t finding tools - it’s connecting them. Forrester found 65% of companies struggle to link error detection outputs back to their annotation platforms. If your workflow has three separate tools, you’re wasting time.

Radiologist celebrating as corrected labels turn red errors into green successes.

Start Here: Your 5-Step Action Plan

Check your current error rate. Use cleanlab on a sample of 1,000 labeled examples. If it flags more than 5%, you have a problem.
Review your labeling guidelines. Are they clear? Do they include visual examples? If not, rewrite them. This alone can cut errors by 47%.
Run one detection method. Start with cleanlab for text or tabular data. Use Encord Active for images. Don’t try to do everything at once.
Set up a correction workflow. Use a shared spreadsheet or annotation tool with audit trails. Assign one person to review flagged items with a domain expert.
Measure impact. Train your model before and after corrections. If accuracy doesn’t improve, you didn’t fix the right errors.

Labeling isn’t a one-time task. It’s a continuous quality control process. The best models aren’t the most complex - they’re the ones trained on clean, correct data. Fix your labels, and you fix your model’s future.

How common are labeling errors in medical datasets?

Labeling errors in medical datasets are significantly higher than in general datasets - averaging 8-15%, compared to 3-8% in non-medical data. Studies show medical images have 38% more labeling errors due to complex anatomy, rare conditions, and subjective interpretations. For example, in lung cancer screening, missing or mislabeled nodules are among the most frequent errors.

Can I fix labeling errors without re-annotating everything?

Yes. Tools like cleanlab and Argilla identify only the most likely errors, so you don’t need to recheck every sample. In practice, teams typically correct only 5-15% of their total dataset after algorithmic detection. The rest are confidently correct. Focus your human effort on the flagged cases.

What’s the difference between cleanlab and Datasaur for error detection?

Cleanlab is a statistical tool that works with any dataset - you feed it predictions and labels. It’s powerful but requires coding. Datasaur is an annotation platform with built-in error detection, designed for non-programmers. It’s easier to use but only supports text and tabular data, not images or object detection. Choose cleanlab for flexibility; Datasaur for speed in annotation workflows.

Why do algorithms sometimes flag correct labels as errors?

Algorithms assume majority patterns are correct. If a disease is rare - say, 1 in 1,000 cases - the model might be trained to ignore it. So when it sees a real case, it thinks the label is wrong. This is called "class imbalance bias." Always validate rare-class flags with domain experts. Don’t auto-delete them.

How long does it take to correct labeling errors in a dataset?

It depends on size and complexity. For a 10,000-image medical dataset with 10% errors flagged, expect 15-25 hours of expert review time. For text datasets, it’s faster - about 2-5 hours per 1,000 flagged examples. Always budget extra time for double-checking and audit trails.

Do I need to retrain my model after correcting labels?

Yes. Even small label corrections can shift the model’s understanding of patterns. After fixing errors, retrain your model from scratch using the corrected dataset. Don’t just fine-tune - the original training may have learned incorrect associations.

Next Steps If You’re Stuck

If you’ve flagged errors but don’t know how to fix them:

For beginners: Start with Argilla’s free tier. Upload 500 labeled examples, run the error detection, and manually correct the top 10. See how it changes your model’s output.
For teams: Set up a weekly label review meeting. Bring 5-10 flagged examples. Let your clinicians, pharmacists, or radiologists decide. Build consensus.
For compliance: Document your correction process. Save versions of guidelines. Log who changed what and why. This is required by FDA and HIPAA for AI medical tools.

Labeling errors are silent killers of AI performance. They don’t crash systems. They just make them wrong - quietly, consistently, dangerously. Fix them before your model goes live.

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