Synthetic Data for AI/ML Development

AI and machine learning thrive on data. Yet accessing high-quality training data remains one of the biggest obstacles in model development. In many cases, only 15 to 20 percent of customers consent to having their data used for analytics. The vast majority of valuable data and the insights it holds remain inaccessible due to strict privacy regulations.

Even when some data is available, quality issues are common. Incomplete, inconsistent, or outdated datasets make it difficult to build effective models. Missing or biased data reduces the accuracy of machine learning algorithms, limits their usefulness, and slows down development efforts.

Synthetic data unlocks new possibilities for training machine learning models across a variety of data-constrained scenarios. It can be applied in distinct ways depending on whether data access, data quality, or data volume is the main challenge.

1. Enabling training when real data is inaccessible

When privacy regulations, consent limitations, or legal constraints prevent access to real data, synthetic data offers a fully compliant alternative. It preserves the structure and statistical properties of the original dataset without exposing any sensitive or identifiable information. This makes it possible to train machine learning models securely, even in highly regulated environments.

2. Enhancing training through data transformation

The synthetic data generation process allows for targeted modifications that are difficult or impossible with real data. For example, rare events or underrepresented classes can be intentionally upsampled to ensure they are adequately captured during training. This improves model performance, especially for edge cases and low-frequency scenarios.

3. Augmenting limited datasets to increase training volume

When the available real-world data is accessible but insufficient, synthetic data can be used to expand the training dataset. By combining real and synthetic samples, teams can create larger, more diverse data pools that support better generalization and reduce overfitting.

These three applications make synthetic data an essential asset in modern AI and ML development, helping teams build more robust models faster and more safely.

Bias in AI is not just a technical flaw but a business and societal risk. According to Gartner, 85 percent of algorithms today are impacted by bias, leading to unfair outcomes in areas such as hiring, lending, insurance, and healthcare. These failures are not only reputationally damaging but also financially costly. With global regulations like the EU AI Act now enforcing fairness and transparency, companies face increasing pressure to demonstrate compliance and explainability in their AI systems. Yet most organizations are unprepared to meet these standards.

Despite growing awareness, the industry remains far behind. Many AI models in production have never been audited for fairness. Common shortcuts, such as removing sensitive attributes from datasets, fail to address underlying bias and often introduce proxy discrimination. This results in opaque decision-making processes that are hard to explain and even harder to trust. Without proactive steps to ensure fairness and interpretability, organizations risk deploying flawed models that alienate users, make poor predictions, and face regulatory scrutiny. Bridging this gap requires a fundamental shift toward responsible AI practices that prioritize transparency and equity from the start.

High-quality synthetic data can play a transformative role in reducing bias and increasing transparency in AI systems. By addressing imbalances in training datasets, synthetic data helps create fairer models that produce more equitable outcomes across different population groups.

MOSTLY AI’s Data Intelligence Platform has demonstrated this impact in real-world scenarios. In one case, synthetic data reduced racial bias in a crime prediction dataset from 24 percent to just 1 percent. In another, it narrowed the income gap between high-earning men and women in the U.S. Census data from 20 percent to just 2 percent. These results show how synthetic data can be used not only to simulate reality but also to actively correct structural bias in datasets. For more examples, explore our Fairness Series.

Synthetic data also enhances explainability by enabling secure access to representative training data during audits. Regulators and reviewers often require insight into the data behind AI models, but sharing original, sensitive datasets is often not permitted due to privacy constraints. Synthetic data offers a compliant alternative. It mirrors the statistical properties of real data while protecting individual privacy, allowing auditors and evaluation teams to investigate model behavior, performance, and potential biases without risk. This makes synthetic data a powerful enabler of transparent and accountable AI.