How to Recognize Labeling Errors and Ask for Corrections in Machine Learning Datasets
Dec, 18 2025
When you're training a machine learning model, the data you feed it is everything. But what if the labels on that data are wrong? A single mislabeled image, a misclassified medical note, or a poorly drawn bounding box around a tumor can throw off an entire system. In healthcare, where models help diagnose diseases or predict patient outcomes, labeling errors aren't just annoying-they can be dangerous. Studies show that even high-quality datasets contain labeling errors between 3% and 15%. In medical imaging alone, error rates can hit 8.2% or higher. The good news? You donât have to accept them. You can find them. And you can fix them.
What Labeling Errors Actually Look Like
Labeling errors arenât always obvious. They donât always say "wrong" in big red letters. Sometimes theyâre subtle. Here are the most common types youâll run into:- Missing labels: An object or entity is in the image or text but wasnât labeled at all. In a chest X-ray, a small nodule might be missed. In a patient note, "hypertension" might be mentioned but never tagged as a medical condition.
- Incorrect fit: The bounding box around an object is too big, too small, or off-center. A tumor might be labeled as half inside and half outside the box. In text, an entity like "aspirin" might be labeled as "aspirin and acetaminophen" when only one drug was mentioned.
- Wrong category: A cat is labeled as a dog. A benign tumor is labeled as malignant. A patientâs diagnosis of "Type 2 diabetes" is tagged as "Type 1".
- Ambiguous examples: A photo shows a blurry shape-could be a tumor, could be a shadow. Annotators disagree. The system doesnât know what to learn.
- Out-of-distribution examples: A dataset meant for adult patients includes a pediatric scan. The model wasnât trained for this, but the label still forces it to fit.
- Midstream tag changes: The team changes the labeling rules halfway through the project. Suddenly, "high blood pressure" is no longer labeled as "hypertension." Old labels become inconsistent.
These errors donât just happen because annotators are careless. They happen because instructions are unclear, examples are insufficient, or the task is inherently ambiguous. TEKLYNX found that 68% of labeling mistakes stem from poor guidelines-not human error.
How to Find Labeling Errors
You canât catch every mistake by eye. Thatâs why tools and methods exist to help you spot them systematically.1. Use confident learning with cleanlab
cleanlab is the most widely used open-source tool for finding labeling errors. It doesnât need you to know the right answers-it just needs your modelâs predictions and the original labels. It calculates how confident the model is in each label and flags ones that seem off. For example, if a model is 98% sure an image is a benign tumor, but the label says malignant, cleanlab flags it. In benchmark tests, it catches 78-92% of errors. It works for text, images, and tabular data.
2. Use multi-annotator consensus
Have three people label the same sample. If two agree and one disagrees, thatâs a red flag. Label Studioâs data shows this cuts errors by 63%. Itâs slower and more expensive, but in healthcare, where lives are at stake, itâs worth it. You donât need all three to be experts-just trained annotators following the same rules.
3. Run your model over the data
Train a basic model on your labeled data, then run it again. Look at the predictions itâs most confident about-but that donât match the labels. If the model says "no tumor" with 95% confidence, but the label says "tumor," thatâs probably an error. Encordâs system finds 85% of errors this way, as long as your model has at least 75% baseline accuracy.
4. Check for class imbalance
Are there 100 images of normal lungs for every one image of a tumor? Algorithms often mislabel rare cases as errors. cleanlab can over-flag minority classes. Always cross-check flagged items in low-frequency categories with a domain expert.
How to Ask for Corrections-Without Causing Chaos
Finding errors is only half the battle. Getting them fixed without breaking workflow or morale is the real challenge.Donât say: "This label is wrong. Fix it."
Do say: "I noticed a potential mismatch here. Can we review it together?"
Start with collaboration, not correction. Use tools like Argilla or Datasaur that let you highlight the flagged item and leave a comment directly on the annotation. Attach the modelâs prediction confidence score. Show the image. Reference the labeling guideline. Say: "According to version 3.1 of our guidelines, a lesion must be fully enclosed. This one is 15% outside the box. Can we adjust it?"
For medical data, involve a radiologist or clinician. Donât let an annotator decide whether a tumor is malignant. Let the expert say: "Yes, this is a benign cyst. The label is wrong." Or: "Actually, this is early-stage cancer. The label is right. The model is wrong."
Keep an audit trail. Every correction should be logged: who changed it, when, why, and what guideline it followed. This isnât bureaucracy-itâs traceability. If a model fails later, you need to know if the error was fixed-or if the fix was wrong.
Tools Compared: What Works Best
Not all tools are created equal. Hereâs what you need to know:
| Tool | Best For | Limitations | Requires |
|---|---|---|---|
| cleanlab | Statistical accuracy, research teams | Steep learning curve; needs coding skills | Model predictions + labels; 1,000+ samples |
| Argilla | Text annotation, academic use | Struggles with >20 labels; not for object detection | Hugging Face integration; web interface |
| Datasaur | Enterprise annotation teams | No support for images or video | Tabular data; 5-50 classes |
| Encord Active | Computer vision, medical imaging | Needs 16GB+ RAM; heavy compute | Images + model predictions |
| Label Studio | Multi-annotator workflows | Manual consensus; no auto-detection | Three annotators per sample |
In healthcare, if youâre working with X-rays or MRIs, use Encord. If youâre labeling patient notes, use Argilla. If youâre managing a team of 20 annotators, Datasaurâs integration saves hours. Cleanlab is powerful-but only if you have a data scientist on staff.
Real-World Impact
A hospital in Sydney used cleanlab to review 8,000 chest X-rays labeled for pneumonia. The system flagged 612 potential errors. After review by radiologists, 387 were confirmed as mislabeled. They corrected them. The modelâs accuracy jumped from 81% to 87%. False negatives dropped by 22%. Thatâs not a small win-itâs the difference between missing a life-threatening infection and catching it early.
Another team at a medical AI startup in Adelaide found that 41% of their entity recognition errors were due to incorrect boundaries in clinical notes. They rewrote their labeling guidelines with visual examples. They trained annotators on the new rules. They used multi-annotator review. Within two weeks, their error rate dropped from 14% to 3.7%.
These arenât edge cases. Theyâre standard outcomes when you treat data quality like a core part of your model development-not an afterthought.
What to Avoid
Donât assume your labels are clean. Donât trust your annotators to catch everything. Donât skip validation because itâs "too slow."
Donât rely only on algorithms. As Dr. Rachel Thomas from USF warns, algorithms can misidentify minority classes as errors. A rare disease label might be flagged as noise-but itâs real. Always pair machine detection with human review.
Donât change labeling rules mid-project. If you do, version your guidelines. Document every change. Otherwise, youâll create a mess of inconsistent labels that no one can fix.
And donât ignore the human side. Annotators arenât machines. They need clear instructions, feedback, and recognition. When they see their corrections directly improve model performance, they care more. Thatâs how you build a culture of quality.
Next Steps
If youâre starting out:
- Choose one dataset to audit-start small. A hundred images. A thousand notes.
- Run cleanlab or your model over it to flag errors.
- Set up a 15-minute review session with one domain expert and one annotator.
- Fix the top 10 errors. Record how they changed your modelâs output.
- Write or update your labeling guidelines based on what you learned.
Repeat. Every time you train a new model, audit the data first. Make it part of your pipeline. Not a step. A habit.
Labeling errors arenât a bug. Theyâre a feature of human work. But with the right tools and mindset, you can turn them from a liability into your biggest advantage.
How common are labeling errors in medical datasets?
Labeling errors in medical datasets are very common. Studies show error rates range from 8% to 15%, with some areas like medical imaging hitting as high as 38% above general computer vision datasets. In one 2023 study, 41% of errors involved incorrect boundaries around lesions, and 33% were misclassified entity types in clinical notes.
Can I fix labeling errors without a data scientist?
Yes, but your options are limited. Tools like Argilla and Datasaur offer user-friendly web interfaces that donât require coding. You can use their built-in error detection features and manually review flagged items. However, for deeper statistical analysis-like using cleanlab-youâll need someone who can run Python scripts or work with APIs.
Whatâs the fastest way to reduce labeling errors?
The fastest way is to improve your labeling guidelines. Clear instructions with annotated examples reduce errors by 47%, according to TEKLYNX. Add visual guides, edge cases, and doâs/donâts. Train annotators on them. Then run a small audit. Fix the top 5 mistakes. Repeat. This often cuts errors by half within a week.
Do I need to relabel my entire dataset?
No. Most datasets have only 3-10% errors. Use error detection tools to find the worst offenders. Focus on the top 20% of flagged items-they cause 80% of model degradation. Fix those first. You donât need to review every single label.
How do I know if a correction actually improved my model?
Train two versions of your model: one on the original data, one on the corrected data. Test both on the same holdout set. If precision, recall, or F1 score improves-especially on rare classes-youâve made progress. Even a 1-2% gain in accuracy can be clinically meaningful in healthcare.
Are there regulations requiring label error correction?
Yes. The FDAâs 2023 guidance on AI/ML-based medical devices requires organizations to validate training data quality, including systematic identification and correction of labeling errors. If youâre building a diagnostic tool, youâre legally expected to have a data quality process in place.
Moses Odumbe
December 18, 2025 AT 14:00Okay but have you tried cleanlab on a dataset with 50k+ images? đ€Ż Itâs not magic-itâs math. And it *will* flag your grandmaâs cat as a tumor if your modelâs overconfident. Iâve seen it. Also, always check class imbalance first. Otherwise youâre just noise-picking. đ§ đ
Meenakshi Jaiswal
December 19, 2025 AT 23:55This is such a vital post-thank you for laying it out so clearly! đ Iâve worked with medical annotators in India, and the biggest issue isnât carelessness-itâs unclear guidelines. One annotator thought âhypertensionâ meant systolic >140, another thought it meant diastolic >90. No wonder errors pile up. Visual examples changed everything for us.
Tim Goodfellow
December 20, 2025 AT 15:59YâALL. I just ran cleanlab on my lung X-ray dataset and it spat out 127 flags. I laughed. I cried. I retrained. Accuracy jumped 8%. This isnât just a tool-itâs a *revolution*. If youâre still manually reviewing labels like itâs 2018, youâre leaving lives on the table. đđ„
mark shortus
December 22, 2025 AT 00:39So⊠youâre telling me the FDA actually CARES about data quality now? đ± I mean, Iâm not surprised-after all, AI is just code written by people who forgot to drink enough water-but this is wild. Iâve seen models fail because someone labeled a shadow as a nodule. And now⊠theyâre gonna make us DOCUMENT it? đ€Ż
Elaine Douglass
December 23, 2025 AT 17:18I love how you said to start with collaboration not correction đ I used to say âthis is wrongâ and people got defensive. Now I say âhey can we look at this together?â and suddenly everyoneâs a team player. Small shift, huge difference. Also Iâm using Argilla now and itâs so pretty đ€
Takeysha Turnquest
December 24, 2025 AT 11:49Labels are just human illusions pretending to be truth
What is a tumor but a pattern our fear has named?
What is a label but a cage for the chaos of biology?
We think weâre fixing errors-but weâre just reinforcing the myth that data can be clean
It never was
It never will be
And maybe thatâs the point
Vicki Belcher
December 24, 2025 AT 15:03THIS. IS. SO. IMPORTANT. đ I work in a startup and we used to skip data audits because âweâre agile.â Now we do a mini-audit before every model sprint. Our false positives dropped 40%. Our radiologists actually smile now. Itâs not just tech-itâs respect. And yes, emojis are mandatory. đȘâ€ïž
Aboobakar Muhammedali
December 24, 2025 AT 16:53cleanlab is great but i dont have a data scientist
argilla works for me
we use it for clinical notes
just flag the weird ones
ask the nurse to check
done
no code needed
thanks for the post
Laura Hamill
December 25, 2025 AT 20:52So now we need to pay for tools AND hire radiologists to check every label? And the FDA is watching? đ Iâm just trying to get my model to predict if someone has a cold. Why is this so complicated? I just want to ship something. đ”âđ«
Alana Koerts
December 25, 2025 AT 23:50cleanlab catches 78-92% of errors? Thatâs not impressive-thatâs lazy. If your model is 90% confident and the label disagrees, maybe your model is garbage, not the label. Youâre outsourcing quality control to a black box. Classic.
pascal pantel
December 26, 2025 AT 21:35Letâs be real: if youâre not using Encord Active for medical imaging, youâre doing it wrong. The rest of these tools are toys. cleanlab? Only if youâre in academia. Argilla? Fine for text. But for X-rays? Encord. Period. Your ROI isnât in accuracy-itâs in liability reduction. Youâre not building a model-youâre building a lawsuit magnet.
Kevin Motta Top
December 27, 2025 AT 08:34From Nigeria to the Bronx-this applies everywhere. We had a team in Lagos labeling malaria slides. No training. No guidelines. Just âis there a parasite?â One guy thought the RBCs were parasites. We fixed it with one visual guide. Error rate dropped from 22% to 4%. Culture > code.
Alisa Silvia Bila
December 28, 2025 AT 00:08Love the emphasis on human side. Annotators are the unsung heroes. I once had one stay late to re-label 300 images because she said âI donât want a kid to miss treatment because of me.â Thatâs the kind of care no tool can replicate.
William Liu
December 28, 2025 AT 06:46Start small. Audit 100 images. Fix the top 5. Retrain. See the difference. Then do it again. Itâs not about perfection. Itâs about progress. One step. Then another. Thatâs how you build trust-in your data, your team, your model.
Aadil Munshi
December 29, 2025 AT 09:46Wow. So weâre supposed to believe that labeling errors are the real bottleneck? Meanwhile, the model architecture is just⊠what? Decorative? đ I mean, sure, cleanlab is cool-but have you tried a transformer on this data? Or are we just going to keep patching garbage in, hoping it becomes gold?