Artificial intelligence models learn from the data they are trained on. If this data does not adequately represent the variety of real-world scenarios, populations, and contexts, the model risks producing biased, discriminatory, or simply inadequate results when used in production.

This article is part of the AI Data Testing chapter of the OWASP AI Testing Guide.

Testing for dataset diversity & coverage verifies that the data used to train and validate an AI model is sufficiently representative and diverse. This verification is essential to ensure the fairness, reliability, and generalization capabilities of the system.

Why dataset diversity is a security requirement

A non-representative dataset is not just a technical problem: it is a vulnerability that can have concrete impacts on people, processes, and regulatory compliance.

When training data lacks diversity, the model tends to replicate and amplify the biases present in the data itself. This results in:

• Discrimination against underrepresented demographic groups
• Systematic errors in contexts not anticipated during training
• Poor performance in real-world operational scenarios
• Loss of user trust and reputational risks
• Non-compliance with data protection and algorithmic fairness regulations

Verifying dataset diversity and coverage allows these gaps to be identified before the model is deployed, reducing operational, legal, and reputational risks.

Testing objectives

Testing for dataset diversity & coverage focuses on three main areas:

• Demographic representativeness: datasets must reflect demographic groups, operational contexts, and real-world conditions in a balanced way
• Scenario coverage: data must include the variety of situations the model will encounter in production
• Regulatory and ethical compliance: datasets must adhere to Responsible AI standards and regulatory constraints applicable to the relevant sector

Methodology and payloads

Analysis of demographic representation

A statistical analysis is conducted to compare the demographic distribution present in the dataset with that of the reference population or expected user base.

This analysis requires:

• Clear definition of sensitive attributes relevant to the application context (age, gender, geographic origin, socioeconomic conditions)
• Measurement of the distribution of these attributes in the training data
• Comparison with the expected distribution in the target population

Vulnerability indicator: some demographic categories are represented significantly differently compared to the actual system users.

Verification of operational scenario coverage

The completeness and variety of scenarios represented in the dataset are evaluated against the model’s expected use.

Examples of scenarios to verify:

• Variable lighting conditions for computer vision systems
• Linguistic and dialectal diversity for natural language processing systems
• Variability of environmental conditions for IoT systems
• Diversity of devices and configurations for mobile applications

Vulnerability indicator: critical real-world scenarios are missing or underrepresented; the model may not correctly handle common situations in the production environment.

Bias detection and fairness measurement

Fairness metrics such as demographic parity, equal opportunity, and equalized odds are used to measure potential imbalances in model results across different groups.

Fairness analysis is conducted on both training data and model outputs, verifying that performance is comparable across different reference groups.

Vulnerability indicator: substantial biases or disproportionate representation of specific groups are identified.

Expected output

An adequately diversified and representative dataset must meet these minimum criteria:

• The distribution of demographic attributes mirrors that of the target population. No relevant group should be represented by less than 5% of the total samples
• The Demographic Parity Difference remains below 15% for all identified sensitive attributes
• The dataset includes transparent documentation (datasheet) describing data sources, composition, collection process, and known limitations
• Operational scenario coverage is complete relative to the use cases expected in production

Remediation actions

When analysis highlights gaps in diversity or coverage, targeted actions must be taken.

Data enrichment

Acquire new data from underrepresented groups, less-represented geographic regions, or missing operational scenarios. This approach is the most effective but requires time and resources for the collection and labeling of new samples.

Expected impact: direct improvement of dataset representativeness with real data that captures the complexity of the operational world.

Data augmentation

Apply data augmentation techniques to artificially increase the variety of existing data:

• For tabular data: SMOTE (Synthetic Minority Over-sampling Technique)
• For text: back-translation and paraphrasing
• For images: geometric and color transformations

It is essential to verify that augmentation techniques do not introduce unrealistic artifacts that could degrade model performance.

Expected impact: increased data variety without the need for additional collection, with care taken not to introduce artificial distortions.

Data balancing

Apply pre-processing techniques such as oversampling minority classes, undersampling majority classes, or re-weighting samples during training. These techniques allow for balancing the influence of various classes on the learning process without modifying the original data.

Expected impact: reduction of class bias and improvement of model fairness across different groups.

Continuous monitoring

Implement continuous integration processes that constantly monitor data distribution and fairness. Perform regular fairness audits to verify that new data added to the dataset maintains the required diversity and representativeness characteristics.

Expected impact: long-term maintenance of dataset quality and timely detection of data distribution drifts.

Documentation

Compile detailed datasheets that document the motivation behind data collection, dataset composition, the collection process, recommended uses, and known limitations. This documentation is essential to ensure transparency and allow for informed assessments of the dataset’s suitability for specific use cases.

Expected impact: complete transparency regarding dataset composition and limitations, facilitating audits and regulatory compliance.

Suggested tools

• AI Fairness 360 (AIF360): IBM open-source toolkit for detecting and mitigating bias in AI datasets and models
• Fairlearn: Python library for assessing and improving the fairness of machine learning models
• What-If Tool: Google tool for visually analyzing ML datasets and models against fairness metrics
• imbalanced-learn: Python library for resampling and balancing techniques for imbalanced datasets

Useful resources

Technical and regulatory resources for further study on verifying AI dataset diversity and coverage:

• Datasheets for Datasets (arXiv:1803.09010): framework for documenting the composition and characteristics of datasets
• A Framework for Understanding Unintended Consequences of Machine Learning: analysis of the unintended impacts of bias in datasets
• NIST Special Publication on Bias in AI: guidelines for identifying and managing bias in AI systems
• EU AI Act Requirements on Data Governance: European regulatory requirements on data governance for AI systems

How ISGroup supports you

ISGroup supports organizations in evaluating and improving the quality of datasets used to train artificial intelligence models.

Through our Secure Architecture Review service, our experts analyze the architecture of AI systems, verify the representativeness of datasets, and identify potential biases that could compromise the fairness and reliability of models.

Our approach combines in-depth technical analysis with an understanding of the regulatory context and Responsible AI requirements, providing concrete recommendations to improve the diversity and coverage of training data.

Frequently Asked Questions

• What is the difference between dataset diversity and coverage?
• Diversity refers to the variety of demographic groups and characteristics represented in the data. Coverage concerns the completeness of operational scenarios and use cases that the model will have to handle in production. A dataset can be diverse but have poor coverage of critical scenarios, or vice versa.
• How is bias measured in a dataset?
• Bias is measured through fairness metrics such as demographic parity, equal opportunity, and equalized odds. These metrics compare model performance across different demographic groups to identify systematic disparities in results.
• How large must a dataset be to be considered representative?
• There is no universal minimum size. Representativeness depends on the complexity of the problem, the number of relevant demographic groups, and the variety of operational scenarios. As a general rule, every relevant group should be represented by at least 5% of the total samples, but in some contexts, higher percentages may be necessary.
• What are the regulatory risks of a non-representative dataset?
• A non-representative dataset can lead to GDPR violations for discriminatory processing, non-compliance with the NIS2 directive for critical systems, and violations of sector-specific regulations requiring algorithmic fairness. Furthermore, it can expose the organization to reputational risks and legal litigation for discrimination.
• How is the composition of a dataset documented?
• Structured datasheets are used to describe: motivation for collection, demographic and statistical composition, collection and annotation process, recommended and discouraged uses, known limitations, and identified biases. This documentation is essential for transparency and regulatory compliance.
• Can data augmentation replace the collection of new real data?
• No, data augmentation is a useful complement but cannot completely replace the collection of real data. Augmentation techniques can introduce unrealistic artifacts and do not capture the complexity of the real world. They are effective for increasing the variety of existing data, but not for bridging fundamental gaps in representation.

References

• AITG-DAT-01: Testing for Training Data Exposure
• AITG-DAT-02: Testing for Runtime Exfiltration
• AITG-DAT-04: Testing for Harmful Content in Data
• AITG-DAT-05: Testing for Data Minimization & Consent

Integrating techniques for diversity analysis, data balancing, and continuous monitoring helps build fairer and more reliable AI models. Regularly testing the representativeness and coverage of datasets is fundamental to ensuring that AI systems maintain consistent performance and respect the principles of Responsible AI in production.