Resuma detalhadamente em português:
How AI Creates Synthetic Data?
Md Faizan Alam
Published in Level Up Coding
Apr 13, 2025

Did you know AI is creating Synthetic Data?

Yes, you heard it right, AI is creating Data. As you know, the AI assistants we use today are nothing but Large Language Models (LLMs) trained on massive real-world datasets. Using this training, these LLMs and GANs(Generative Adversarial Networks), with their algorithms, generate similar data replicating real-world data but not belonging to any real-life person, and this is called Synthetic Data.

This not only provides us with more helpful data but, as it doesn’t really belong to any individual, it prevents the issue of privacy or leaking of sensitive information.

Why Synthetic Data Matters?
Real-world data is limited. There is only a finite amount of data available for the training of GANs and LLMs and usage for other purposes, and on top of that, if we remove the sensitive and private data that cannot be used in any conditions, it drops the total even further.

Here comes synthetic data, with which we can generate any amount of data exactly like real-world data for our requirements, and the best part is that it is far cheaper than real-world data.

Another advantage that synthetic data has over real-world datasets is bias. Real-world data may favor certain demographics or opinions, but synthetic datasets are balanced and do not have any bias whatsoever.

How AI Creates Synthetic Data?
Artificial Intelligence (AI) plays a pivotal role in generating synthetic data, leveraging advanced computational techniques to produce datasets that mimic the statistical properties of real-world data. Below are the detailed methods and technologies AI uses to create synthetic data:

Statistical Distribution Methods
Process: Real-world data is analyzed to identify its statistical characteristics, such as mean, variance, and distribution type (e.g., normal, exponential). Algorithms then generate new data points that follow these patterns.
Advantages: This method is simple and efficient, making it ideal for structured datasets like tabular data.
Limitations: It may fail to capture complex relationships or nuances in the original data12.
Model-Based Techniques
Overview: Machine learning models are trained on real-world data to understand its structure and statistical properties. Once trained, these models generate synthetic datasets that replicate the original data’s patterns.
Applications: Useful for creating hybrid datasets by combining real and synthetic elements.
Benefit: It provides greater flexibility in generating realistic data14.
Deep Learning Methods
AI employs sophisticated deep learning techniques for generating high-quality synthetic data, especially for complex types like images, videos, and time-series data:
Generative Adversarial Networks (GANs)
Mechanism: GANs consist of two neural networks — a generator and a discriminator — working adversarially:
The generator creates synthetic samples starting from random noise.
The discriminator evaluates these samples against real-world data, providing feedback to improve the generator’s output.
Outcome: GANs produce highly realistic synthetic datasets indistinguishable from actual data, such as photorealistic images or videos.
Use Cases: Commonly used in computer vision applications, such as training autonomous vehicles or facial recognition systems12.
Variational Autoencoders (VAEs)
Mechanism: VAEs use an encoder-decoder architecture:
The encoder compresses input data into a latent representation.
The decoder reconstructs new synthetic samples from this representation using probabilistic methods.
Outcome: Generates realistic yet varied datasets while preserving the statistical properties of the original.
Use Cases: Ideal for creating synthetic images with slight variations or augmenting datasets for ML tasks13.
Transformer-Based Models
Mechanism: Generative Pre-trained Transformers (GPT) are trained on large text datasets to learn language structure and generate coherent sequences based on input prompts.
Outcome: Produces synthetic text data resembling real-world linguistic patterns.
Use Cases: Widely used in natural language processing (NLP) tasks like chatbots or document generation13.
4. Agent-Based Modeling
Process: Simulations involve individual agents interacting within predefined systems to mimic specific scenarios or behaviors. For example, this method can simulate customer interactions in retail environments or traffic patterns in urban areas.
Advantages: Useful for generating scenario-specific datasets that are difficult to obtain from real-world observations
Applications of Synthetic Data
Synthetic data has diverse applications across industries, enabling innovation and solving challenges related to privacy, bias, and data scarcity. In healthcare, it is used to simulate patient records, medical imaging, and genetic data for research and algorithm testing without compromising privacy.

Finance leverages synthetic data to test trading strategies, risk models, and fraud detection systems by simulating market behaviors and transaction patterns. In autonomous vehicles, companies like Tesla and Waymo use synthetic environments to train self-driving algorithms in rare or dangerous scenarios.

Social sciences benefit from synthetic datasets to analyze trends, habits, and social interactions while preserving privacy. Additionally, synthetic data augments machine learning datasets, addressing issues like imbalance and bias while improving model performance.

Challenges of Synthetic Data
Generating synthetic data comes with technical and practical challenges. Ensuring data quality is critical; synthetic datasets must accurately replicate real-world patterns while avoiding errors that could impact AI model performance.

However, this often involves a trade-off between privacy and accuracy. Technical expertise is required to create realistic data that captures complexities like outliers or anomalies present in real-world datasets. Stakeholder confusion can arise due to the novelty of synthetic data; some may distrust its relevance or overestimate its capabilities. Clear communication about its limitations is essential.

In healthcare, the stakes are higher due to potential liabilities from modeling errors or distrust among practitioners regarding clinical decisions based on synthetic datasets.

Ethical Considerations
Synthetic data raises ethical concerns regarding fairness, transparency, and privacy. Bias amplification is a significant risk; if original datasets are biased, synthetic data may perpetuate or even magnify these biases in AI models.

Ethical practices must focus on identifying and mitigating such biases during generation processes. Transparency around how synthetic data is created and used is crucial for building trust. Although synthetic data bypasses the need for consent in many cases, marketers and researchers must ensure informed consent when real-world data is involved in its generation.

Privacy risks like re-identification — where synthetic data inadvertently reveals sensitive information — must be addressed through techniques like differential privacy or anonymization.

Furthermore, compliance with regulations such as GDPR requires careful audits and risk assessments to ensure the ethical use of synthetic data in line with legal standards