Dataset QA & Label Audit Services
Independent labeled-dataset audits with per-class error rates, class confusion analysis and train/test leakage detection, including other vendors' work.
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What Is a Dataset QA & Label Audit?
A statistically designed review measures actual error rate with confidence intervals and finds where errors concentrate. Annotation creates labels; an audit checks existing labels.
Yes—Including Datasets Someone Else Delivered
The review is independent of remediation. The evidence can be acted on by your vendor, your internal team or eQOURSE.
What an Audit Checks
- Label correctness
- Consistency and class confusion
- Coverage and balance
- Train/test split integrity
- Guideline quality
- Metadata and provenance
How Big Does the Audit Sample Need to Be?
| Expected error | Precision | Approximate sample |
|---|---|---|
| 5% | ±2% | 460 |
| 5% | ±1% | 1,825 |
| 10% | ±2% | 865 |
| 10% | ±1% | 3,460 |
| 2% | ±1% | 750 |
Per-class reporting requires stratified coverage and deliberate sampling of rare classes.
The Diagnostic That Determines the Fix
Scattered errors, systematic class confusion, reviewer outliers, temporal drift and universal ambiguity each require a different response.
Is Your Evaluation Score Real?
We check exact and near-duplicate overlap, shared-source derivatives, augmentation lineage and chronological split integrity.
How an Audit Runs
- Scoping
- Guideline review
- Stratified sample design
- Blind review
- Adjudication
- Analysis
- Report and recommendation
Modality-Specific Label Audits
Image Annotation Services Video Annotation Services Text and NLP Annotation Services Audio and Speech Annotation Services Document and OCR Annotation Services 3D Point Cloud and LiDAR Annotation Services LLM and RLHF Annotation Services
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Frequently Asked Questions
What is a dataset QA and label audit?
A statistically designed review of existing labels that measures error rate, class-level quality and the cause of defects.
Can you audit another vendor's dataset?
Yes. Findings are evidence-led, confidential and actionable by your current vendor, your team or eQOURSE.
How many items do you need to audit?
Sample size depends on the precision required. Per-class rates require stratified coverage, especially for rare classes.
Why does dataset size have little effect on sample size?
Sampling precision mainly depends on how many items are reviewed. A finite-population correction can reduce the requirement for smaller datasets.
How do you find label errors without reviewing everything?
We combine stratified manual review, consensus re-labeling, adjudication, agreement recomputation and rule-based checks.
Can a model find label errors on its own?
A model can prioritise likely problems, but a human reviewer confirms every error against the agreed guideline.
What is train/test leakage?
It is overlap between training and evaluation data that makes evaluation measure memorisation rather than unseen performance.
How does split leakage happen?
Common causes include deduplicating after splitting, separating augmented copies and putting related frames or source derivatives in different splits.
How do you distinguish an annotator problem from a guideline problem?
Scattered errors often indicate attention pressure, repeated class confusion points to unclear rules and universal disagreement suggests taxonomy ambiguity.
Should we correct or re-label?
Sparse errors may need selective correction, concentrated defects suit targeted repair, and high error density can make re-labeling more economical.
Which data types can you audit?
Image, video, text and NLP, audio and speech, document and OCR, 3D point cloud and LiDAR, and LLM or RLHF evaluation data.
Will you tell us if the dataset is already in good shape?
Yes. If the evidence does not justify more work, the recommendation can be to leave the dataset alone.
Are audit findings confidential?
Engagements can use NDAs and controlled-access workflows, and findings are not used in marketing without written permission.
What does a label audit cost?
Pricing depends on sample size, required precision, number of classes, modality, consensus depth, domain expertise and security requirements.
How do we start?
Share a representative sample and the annotation guideline for a directional audit and recommendation.