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Data labels can be wrong in several distinct ways: an individual example may be mislabeled, the annotation rules may be unclear or inconsistently applied, the target may encode a biased judgment or weak proxy, or the data may be incomplete or poorly measured. A label can be applied consistently and still fail to represent the real-world concept a model is meant to learn.
What data labels tell a machine-learning model
A label is the answer attached to an example that a model is trained to predict: for example, whether an image contains a bicycle or whether a message is spam. In practice, labels also define the task. The taxonomy, instructions, reference standard and decisions about edge cases determine what counts as the right answer.
That definition needs to be precise. Google’s data-quality guidance recommends examining what the data literally communicates, what it leaves out, and how collection conditions affect it. A term such as “toxic,” “healthy” or “qualified” may seem clear until annotators have to apply it to borderline examples.
Label quality matters at two points in the machine-learning lifecycle. During training, labels supply the learning signal; errors can teach a model the wrong association. During testing, labels determine which predictions are counted as correct, so errors can make evaluation misleading. The size and direction of the effect depend on the task, data and pattern of errors; noisy labels do not make every model fail in the same way.
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Four different problems people call “bad labels”
1. A factual mistake on an individual example
An annotator may mark a cat as a dog, or a transcription may assign the wrong word to a recording. This is a direct mismatch between the example and its label. Such mistakes may be isolated, or they may cluster in a particular class or source.
2. Ambiguous rules or inconsistent application
Two annotators can see the same example and choose different labels because the instructions do not define a boundary, or because they interpret a subjective category differently. In that case, disagreement is not necessarily evidence that one person made a careless mistake; the task definition itself may not settle the answer.
3. A biased judgment or weak proxy
A label may faithfully record a past decision without measuring the outcome a model is supposed to predict. For example, a historical decision can encode the judgments and practices of the institution that made it. Similarly, a proxy can be easy to record but only loosely related to the intended concept. A model trained on such a label may learn to reproduce the decision or proxy rather than the underlying goal.
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A 2024 study of two annotation tasks found that labeler demographics affected both subjective face annotations and accuracy-based bounding-box annotations. The authors caution that simply recruiting a more diverse group of labelers does not, by itself, establish that the problem is solved; the findings are specific to the tasks and samples studied. The study recruited 98 participants for the face-labeling task and 210 for bounding boxes. Read the study in AI and Ethics.
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Some apparent label problems are actually problems with how examples were collected, measured or recorded. Missing values, instrument error, sampling choices and feature measurement can distort a dataset even if its labels follow the written rule consistently. It is important to distinguish these issues from a label that is simply incorrect, because they call for different remedies.
Why consistent labels can still be wrong for the task
Consistency answers whether people apply a rule in the same way; validity asks whether the rule measures the concept that matters. A team could agree perfectly on a label that records a historical decision, for instance, while that decision remains a poor or biased target for a future system. Agreement is useful evidence about process, not proof that the target is fair, accurate or suitable.
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That distinction also matters for fairness analysis. Liao and Naghizadeh’s 2023 analysis of labeling and measurement errors found that the impact of biased data varies by fairness criterion: some constraints are more robust to particular biases, while others can be significantly violated. A fairness metric applied without understanding how labels were created can therefore provide false reassurance. Their experiments used FICO, Adult and German credit-score datasets; they do not establish a universal rate of label errors. Read the AAAI paper.
How label errors affect training and evaluation
Errors in training labels can push a model toward incorrect patterns. Google Research’s work on controlled noisy labels reports that label errors can greatly reduce accuracy on clean test data, and describes how deep networks can memorize training-label noise. Its experiments also show why real-world web noise should not be treated as identical to simple random label flips. These are research findings, not a guarantee that every dataset with noisy labels will suffer the same amount of damage. Read the Google Research paper.
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The benchmark in that work examined nearly 213,000 web-collected images, with each image reviewed by three to five annotators. The researchers also constructed ten benchmark datasets with noise levels from 0% to 80% by replacing clean training images with incorrectly labeled web images. Those percentages describe controlled benchmark conditions; they are not estimates of how often ordinary production datasets are mislabeled.
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Errors in test labels create a separate problem: a correct prediction may be scored as wrong, or an incorrect prediction as right. Model metrics then describe agreement with the flawed test annotations rather than performance against the intended real-world target. Cleaning training data without checking evaluation labels can leave that problem hidden.
How to check whether a dataset’s labels are reliable
- Define the intended target operationally. Specify what evidence qualifies an example for each label, which cases are edge cases, and whether the label is an observable fact, a subjective judgment or a proxy. If reasonable reviewers cannot apply the rule consistently, revise the instructions before treating disagreement as individual error.
- Trace how labels and examples were produced. Record who labeled the data, when, under which instructions and measurement process, and whether definitions changed. Check for missingness, sampling bias, instrument problems and changes in collection conditions as well as mislabeled examples.
- Measure disagreement, then inspect it. Agreement statistics can show where annotators diverge, but the score alone cannot tell you whether the target is valid or unbiased. Examine class-specific and group-specific disagreement, and review the rules around clusters of disputed examples. A 2024 analysis of natural-language dataset annotation practices found common errors in the use of inter-annotator agreement and annotation error rates, underscoring the need to interpret such metrics carefully. Its findings concern NLP dataset creation. Read the Computational Linguistics study.
- Audit examples against an appropriate reference. Where a trustworthy reference standard or qualified adjudicator exists, review a sample against it. Prioritize ambiguous, high-impact, outlier and model-disagreement cases. Automated error-detection methods can help surface examples for human investigation, but a flag is not ground truth. See the review of annotation-error detection methods.
- Correct labels with a documented process. Preserve provenance, record why a label changed, version the rules and retain enough information for later users to understand the correction. Then re-evaluate model performance and relevant fairness measures against the cleaned data.
Why there is no universal label-cleaning fix
The right response depends on the error pattern, task and available reference standard. A method that works for isolated random mistakes may miss systematic errors; removing examples flagged as unusual can also discard valid rare cases. Subjective and multi-label tasks may need a different review process from tasks with an objective reference. Human review takes effort, and adding annotators alone does not guarantee better labels.
A 2022 Nature Communications study of active relabeling found that the structure of label errors can affect how effective cleaning strategies are, not just the average error level. There is therefore no evidence-backed single error threshold or algorithm that works as a universal fix. Read the study on active label cleaning.
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Before choosing a cleaning approach, consider whether errors appear random or systematic, whether a trustworthy reference exists, whether class- or group-specific patterns can be inspected, and how much human review is feasible. Also weigh the risk of dropping valid rare examples and whether the process can be reproduced and documented.
What to record when you clean a dataset
- The label definitions, annotation instructions and reference standard used, including edge-case rules.
- Who collected or labeled the examples, when the work occurred, and any changes in process or definitions.
- How disagreement and suspected errors were measured, sampled and adjudicated.
- Which labels changed, why they changed and which dataset version contains the change.
- How the cleaned labels affect model evaluation and relevant fairness measures.
Google’s data-quality guidance recommends documenting dataset corrections so people using the data can interpret its limitations and history. Keeping that record also helps distinguish a genuine improvement in labels from a change in the task definition.
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