The Pros and Cons of Using Synthetic Data for Training AI

Artificial intelligence (AI) models—specifically, generative AI (GenAI) models—are becoming increasingly relevant for today’s businesses, yet many questions remain about how such models work and how accurate they are. Consider, for example, the need to expose an AI model to large amounts of data for training. When data may not yet exist or may lack comprehensiveness, synthetic data comes into the training equation.

What is synthetic data and how has it been used?

Synthetic data is not a new concept. Organizations have been building such data sets for many years for many purposes. With the arrival of new AI models and GenAI models, the term “synthetic data” takes on a new meaning.

Performance testing and scalability scenarios are two common uses for synthetic data. In addition, numerous scientific scenarios and other applications rely on synthetic data to explore new possibilities and run simulations as synthetic data can represent hypothetical situations that go beyond what real-world data may represent.

How is synthetic data created and what does it represent for AI training?

Synthetic data falls into two classifications: non-AI data simulation, or test-data creation using test-data management tools, and AI-generated synthetic data, which can be broken down into structured and unstructured data.

Structured data (which includes structured data sets such as databases, spreadsheets, etc.) can be used differently for machine learning and training purposes.

AI-based systems can take a different approach to analyzing data sets that involve ingesting data as a complete element, not individual data points. That context-based approach has advantages, such as attributing additional meaning to the data without exposing personal or sensitive information and further obfuscating the content of specific records. The information then can be used to generate artificial scenarios that represent a possibility to further train AI.

For training AI, synthetic data uses a base data set of actual historical events or transactions and then creates a synthetic representation of that data and builds upon it. This proves to be a powerful scenario with implications across multiple business cases.

When training autonomous systems like self-driving vehicles, synthetic data becomes the key to exposing those systems to potential events without creating additional risks. If the AI managing autonomous systems were trained only on historical or actual events, it might not be able to identify possible outliers and might make incorrect decisions, which could result in more accidents or failures in AI-driven systems.

Potential outcomes

The goal with synthetic data for AI systems is to help those systems learn about potential outcomes or for AI to become more proactive while considering potential occurrences based on “what if” scenarios. Those scenarios can give AI the ability to automate responses to unforeseen circumstances much more quickly than a human can.

For example, in financial markets, AI is frequently used to gauge particular business outcomes using synthetic scenarios such as supply chain disruption, inflationary factors and stock market volatility. What’s more, synthetic data can be used to train AI to react to customer activities analyzing shopping patterns, online activities and more. Here, synthetic data is created from actual data but sanitized to remove any potential personal information or unintentional bias. An AI system can use that synthetic data to guide customers during purchases and inquiries.

Multiple use cases

For those looking to get the most out of their AI system, synthetic data proves useful when real historical data is scarce, sensitive or difficult to obtain. Other uses include:

• Optimizing privacy and security: By using synthetic data instead of real data, organizations can protect sensitive user information while still training effective AI models.
• Improving data diversity: Synthetic data can augment limited real data to create more comprehensive and representative training sets, introducing more diverse data into the AI training.
• Bias reduction: Synthetic data provides for the introduction of controlled biases that can identify unintended biases in the model, which can be further mitigated by analyzing the algorithms.
• Improving efficient use of available resources: Generating synthetic data can be more resource-efficient than collecting, processing and storing large volumes of real data.
• Promoting innovation and advancing research: Researchers can use synthetic data to explore new ideas, develop new products and further advance AI models without creating privacy concerns.
• Trend forecasting: AI models trained on synthetic data can simulate and predict future data trends, aiding in decision-making.
• Validation testing: AI algorithms can be tested using synthetic data to identify potential problems with AI models without putting real data at risk.

However, those who train AI systems need to be cautiously aware of how synthetic data can create false results. Those risks include:

• Lack of realism: Synthetic data may lack the complexity and nuances of real-world data, leading to AI models that are unable to perform well in real-world scenarios.
• Limited generalization: AI models trained solely on synthetic data might not generalize effectively to real-world situations, due to disparities between synthetic and actual data.
• Ethics concerns: Certain applications, such as medical diagnoses, may raise ethical concerns due to the potential risks inaccurate models pose.
• Model degradation: Using synthetic data for AI model training without periodically syncing it to its underlying real-world data could cause the AI model to degrade more quickly and collapse over time. Synthetic data may lack the diversity and feature distribution of underlying “real” data and tends to exaggerate imperfections, resulting in lower-quality training set data over time. Using sound synthetic data-generation techniques and monitoring the results to ensure that generated synthetic data stays in sync with real-world data (with similar distribution and characteristics) is key.
• Bias introduction: Without careful generation of synthetic data, the data may introduce biases into the training data, resulting in faulty AI models.

Those looking to embrace synthetic data for training and development of AI models must remain keenly aware of the risks involved and the potential weaknesses synthetic data can introduce into the training process. Developers will need to consider the impact that may have on the training of the AI model and account for the potential missteps that could be introduced by a lack of data diversity or context.

This blog was originally posted on Forbes.com.

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