There is no doubt that machine learning (ML) is transforming industries across the board, but its effectiveness depends on the data it’s trained on. The ML models traditionally rely on real-world datasets to power the recommendation algorithms, image analysis, chatbots, and other innovative applications that make it so transformative. 

However, using actual data creates two significant challenges for machine learning models: lack of sufficient data and bias. These two issues limit the potential of ML algorithms, which is why the tech industry is turning to synthetic data as a solution. 

The Rise of Synthetic Data

Synthetic data, or computer-generated artificial information, can mimic real-world scenarios, filling data gaps and guarding against bias. With an increasing shortage of high-quality, real-world data, synthetic information is essential to the future of machine learning. Fake data can unleash the full potential of ML models.

The need for more high-quality data is driving the synthetic data generation market, which is projected to increase by a CAGR of 35.3% annually through 2030. This market is driven by the need to train AI/ML models and vision algorithms, develop predictive analysis solutions, and more.

Understanding Synthetic Data Generation 

Whether the data generated is tabular, image, text, or video-based, or partially, fully, or hybrid synthetic, skilled programmers can mirror real-world data using a few different strategies:

• Rule-based methods: Programmers can create synthetic data based on predefined rules and constraints. 
• Statistical models: Programmers can generate synthetic data using real-life statistical distributions and relationships. 
• Agent-based models: Programmers can simulate the behavior of individual agents to generate dynamic synthetic data. 
• Deep learning models: Programmers can use Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and other generation tools. 

Benefits of Synthetic Data

Creating artificial data offers compelling advantages like reduced cost, greater efficiency, and regulatory compliance. It is cheaper to produce than real-world data and comes automatically labeled, reducing the time required to acquire high-quality datasets. Additionally, it avoids many privacy issues real-world datasets generate by infringing on personal identifiable information (PII) or protected health information. Finally, it can make ML models more accurate by providing better edge cases and testing system boundaries and performance with rarer scenarios than real-world data typically provides.

Mitigating Bias with Synthetic Data

Most importantly, synthetic data combats bias that exists in the real world. Without artificial data, machine learning models could merely replicate the existing inequities reflected in real-life scenarios. Computer-generated data, however, injects the necessary variety into an existing dataset to better reflect diversity. For example, if a real-world dataset represents a majority ethnic group over a minority group because of unconscious discrimination, synthetic data could inject statistical representations of the minority group that fill gaps in the dataset and augment the results. 

ML Applications of Synthetic Data

Image and Video Data 

Synthetic images and videos can enhance machine learning applications for automotive, medical, or other industries that need robust visual data but are limited by privacy concerns or unavailability. They can enhance these industries by optimizing object detection and recognition task performance.

In 2021, biomedical researchers created varying synthetic images of renal cell carcinoma. They trained a GAN model with 10,000 actual photos and then compared the model with another using both real and artificial data. Using fidelity testing to determine its accuracy, they found that the fake images closely matched actual data, suggesting that it could augment machine learning applications in medical classification and diagnostic procedures facing a lack of actual data.

Text Data

Anyone who has used ChatGPT to write a text, email, or long-form article is already familiar with the ability of ML models to produce synthetic text. In relation to fake data generation applications, artificial text can be used for natural language processing (NLP), conversational AI, document generation, data anonymization, and more. 

According to a paper published at the Conference on Language Modeling, synthetic text can also improve the generalization capabilities of language models by providing a variety of text scenarios for the models to learn from. For example, synthetic text data could help expand the variations of language an ML model is exposed to in multilingual learning applications by “ensuring a balanced representation of different classes,” thereby combatting representational bias.

Tabular Data

Tabular data, with its unique row-column organizational structure, is especially beneficial for ML applications in industries where relationships between categorical and numerical variables are crucial, such as finance, healthcare, and retail.

Some use cases include generating synthetic financial transactions to train models for detecting fraudulent activity, creating credit histories to evaluate creditworthiness, developing risk models, and generating synthetic customer data to understand and predict customer behavior. 

Time Series Data

On the other hand, time series synthetic data is different because of its temporal organizational structure. Emphasizing the importance of the order in which data points are recorded enables trend analysis, forecasting, and anomaly detection over time, making it relevant for finance, weather forecasting, IoT sensor data, maintenance, and more. 

For example, a skilled programmer could generate synthetic sensor data to train models for predicting equipment failures and optimizing maintenance schedules, enabling effective predictive maintenance. A programmer could also create financial time series data to test and improve trading algorithms in the finance industry.

Challenges and Considerations in Synthetic Data Generation

Synthetic data generation can powerfully address issues of bias and scarcity, but it is not without its challenges. Adept programmers can avoid common setbacks associated with it by carefully implementing strategies to ensure high-quality synthetic data. 

First, programmers can incorporate fidelity testing methods into their generation process. In comparing fake data with human-annotated, real-world data, fidelity testing ensures that the artificial data accurately reflects real-world statistical properties and complexities and aligns with real-life applications.

Next, if not carefully designed, synthetic data can replicate existing inequalities rather than combat them. Programmers need to avoid introducing biases that might be picked up from the real data the models are trained on. Synthetic data creators should apply robust evaluation and validation procedures to continuously evaluate the quality and representativeness of artificial data.

Earlier this year, Forrester released a report saying that bias, privacy, and regulatory compliance are major obstacles to Gen AI adoption. However, unlike real-world data, synthetic data doesn’t contain private, personal information, can’t be easily reverse-engineered, and can be used in data anonymization. Programmers can also proactively address potential legal and ethical concerns related to the use of synthetic data, especially for sensitive domains like healthcare and finance. 

Driving the Future of AI

Synthetic data is driving the development of AI models into the future. Advancements in generative models, hybrid approaches, domain-specific applications, and increased adoption and integration strategies will shape the future of synthetic data generation. 

Generative models like GANs and VAEs will continue improving, creating more realistic and diverse artificial data. Future strategies will increasingly combine synthetic with actual data to facilitate more comprehensive training datasets. More specialized tools and techniques will be developed for tailoring synthetic data to specific industries and applications, and finally, synthetic data generation will be adopted across even more sectors.

Enduring Importance of Synthetic Data

By addressing two of the most critical challenges in machine learning – data scarcity and bias – synthetic data generation pushes the boundaries of what ML can achieve across industries by providing a scalable and flexible way to augment real-world data. It also enables the development of more accurate and robust models to push AI to greater heights. 

However, synthetic data generation must be used responsibly for the greatest impact. Ethical strategies are paramount to preventing the perpetuation of existing biases encoded in real-world data and ensuring data security, quality, and integrity. Continued research and innovation on computer-generated data will build a future where ML models are more capable, fairer, accurate, and representative of the diversity that makes our world beautiful.