Synthetic Data in AI

What is Synthetic Data?

AI systems, regardless of model type, rely on access to real-world data for training. During training, a model’s parameter values (the model’s internal learned variables that determine overall model behavior and how inputs are transformed into outputs) are numerically adjusted based on the model’s response to real-world information.

Real-world data can influence how a model achieves its predetermined training objective. Overall, real-world data is critical for AI development. 

However, establishing uninterrupted access to real-world databases external to a model’s training environment is becoming increasingly complex for most organizations.

User trust and access cost are two significant factors driving this. Social platforms generate massive volumes of user-centric data that are prime for use in model training. 

Nonetheless, end-users don’t want unauthorized use of their personal data, and even when they know that their data is being collected for AI training, how it’s used for training, and how it’s stored or for how long, is often opaque. 

Additionally, AI developers are having trouble accessing databases with mitigated bias or well-documented bias without paying excessive access fees.

These databases are often proprietary with privately curated datasets that are costly to produce, maintain, and govern. These costs are passed on through restrictive licensing and access fees, expenses that unprofitable AI companies can’t afford in the long term. 

What is synthetic data?

To combat this, synthetic datasets are on the rise. Synthetic data is artificially generated from real-world events and designed to statistically resemble real-world data, making it easier to train models.

Synthetic data can improve model training by simulating real-world events without revealing identifying information from the underlying real-world dataset. 

Artificially generated data enables models to reduce user privacy risks and address costly database access, as synthetic data omits identifying information by mimicking it with obfuscated data points and doesn’t contain sensitive, proprietary information. 

Risks of Synthetic Data

Synthetic data isn’t without its inherent risks, and if the systems we use to automate productivity are artificial and the data powering those systems is artificial, then is the authenticity of human ingenuity on the decline? 

An overreliance on synthetic data can create false perceptions of real-world events. If you’re relying on artificial data to simulate a real-world event, while high-quality synthetic data can somewhat approximate real-world events, the simulation will never be able to capture real-world variability fully.

This can degrade model performance over time, as models that train exclusively on synthetic data and are never exposed to real-world data cannot effectively infer the unpredictable dynamics of real-time datasets. 

Synthetic data can also amplify existing biases. If a real-world database from which synthetic data is generated contains bias, the synthetic set may reproduce and exacerbate that bias by relying on it to make false assumptions. Additionally, poorly generated synthetic data can leak sensitive information from the underlying real-world dataset it’s replicating. 

Synthetic data is poorly generated when it fails to accurately represent the structure of the real-world dataset it is mimicking. Poor generation can lead to overfitting, in which specific data points in the real-world dataset are overemphasized and, when generated artificially, are nearly identical to their real-world counterparts. 

If unverified, the artificial set, which is nearly identical to the original, can be widely circulated and leaked, as it contains identifying information about the real-world dataset used to generate the synthetic one. 

Conclusion 

Synthetic data has emerged as a generative solution to the constraints imposed on models by the need for uninterrupted access to databases with well-documented biases in their training data.

Access to these databases is costly for AI developers and often restricted due to conflicts of interest with database privacy regulations that govern how user data can be harnessed for model training, whose purposes are usually convoluted and not clearly stated. 

Synthetic data is generated from real-world events and designed to represent them statistically. When carefully generated and validated, synthetic datasets can improve model training by maintaining the integrity of the underlying real-world dataset through omitting sensitive and identifying data during generation.

Furthermore, synthetic data enables models to simulate real-world contexts without requiring access to established proprietary databases.   

However, it is essential to understand that overreliance on synthetic data risks degraded model performance, as biases from original real-world datasets can be reproduced and amplified. As well, synthetic data cannot fully account for real-world variability.

It’s not about whether the data that AI models are trained on is real or fake; it’s about adopting artificially generated data alongside real-world data in a balanced way, ensuring the synthetic data is used transparently and audited rigorously.