Synthetic Data and Ω-Robustness: Shaping Reliable AI Through Tail Risk Modeling

As artificial intelligence systems become increasingly complex, synthetic data is increasingly being used to fill the gaps of scarce or sensitive real-world data. However, synthetic data can also bring new problems, such as biases, unrealistic scenarios and hidden risks. To address these challenges, the Ω-Robustness framework carefully utilizes synthetic data to simulate extreme events and enhance the reliability of artificial intelligence, outperforming average performance.

Synthetic Data

Synthetic data is artificially generated through algorithms (such as simulation modeling, generative models), and is similar to real data in structure and features.

There are many ways to synthesize data, mainly including:

• Rule-based simulation: Developers use physical or logical rules to create data, such as:

  • Traffic simulation

  • Financial market simulation

  • Synthetic medical images

• Machine learning-based generative models: Algorithms that learn from existing data to produce new, synthetic data.

Generative Adversarial Network (GAN)

Generative Adversarial Network (GAN) is a method for generating highly realistic image, text or speech data through adversarial training.

Figure 2. Workflow of a Generative Adversarial Network (GAN)

The illustration shows the structure of GAN: The generator generates fake samples, and the discriminator compares the real data and provides feedback to help the generator optimize.

• GANs consist of two networks:

  • Generator: Produces synthetic samples.

  • Discriminator: Distinguishes real from fake data.

• Generator and Discriminator compete and improve during training.

• GANs address real data shortages, especially for rare or sensitive samples.

• They support data augmentation, sample balancing, and robustness testing.

• GANs drive advances in AI creative fields like image generation, speech synthesis, and text creation.

Variational Autoencoder (VAE)

The variational autoencoder (VAE) is a probabilistic generative model consisting of an encoder and a decoder.

• VAE does not directly copy the input data. Instead, it learns a continuous latent space, samples new points from it and decodes to generate data.

• VAE realizes the compression and generation of complex data and is capable of generating new samples that are close but not repetitive.

• It ensures coherence and smoothness of generated data, making it suitable for:

  • Style transfer

  • Data completion

  • Latent variable analysis

• VAE also supports low-sample learning and noisy data modeling, enhancing model robustness under complex distributions.

Diffusion Models and Flow-based Models

• Diffusion Model: By gradually adding noise to the data until it becomes pure noise, and then reverse learning to restore the original data.

• Flow Model: Construct a series of reversible transformation functions to achieve traceable and reconfigurable mappings between the original space and the latent space.

• Comparison

  • The diffusion model pays more attention to the generation quality.

  • The flow model pays more attention to modeling accuracy and reversibility.

• Conditional generation is possible by adding information like text or posture.

• The generation process is continuous and controllable, ideal for high-resolution, fine-texture tasks.

• Compared to GANs, diffusion and flow models train more stably and are less prone to mode collapse.

• They are widely used in image generation, scientific simulation, and precise modeling, producing realistic samples stably and controllably. While these generative models enhance synthetic data creation, they do not inherently ensure robustness against rare, high-risk scenarios — a gap that Ω-Robustness seeks to fill.

Potential Risks

• Many high-risk events are hidden in the tail areas with extremely low probabilities but serious consequences, which are difficult to be covered by conventional training data.

• Synthetic data methods help construct a complete uncertainty space ranging from conventional to extreme.

• Traditional AI training is difficult to capture the complex nonlinear coupling and unexpected combinations among systems.

• The Ω-Robustness framework combines Pareto optimization and sub-robustness to evaluate the performance of the model in extreme scenarios.

• Ω-Robustness focuses on the tail of the input distribution to identify the risk of rare and catastrophic errors.

• At the same time, comprehensively balancing accuracy, model complexity, and resilience under extreme conditions enables a more realistic and conservative evaluation of the model.

• It is particularly important in high-risk fields such as autonomous driving, financial forecasting, and security AI.

Look ahead

Real-world data often falls short of AI's growing demands for security and stability in high-risk scenarios. Synthesis data is hard to predict and may increase the risks of using AI.  Ω-Robustness generates extreme scenarios through synthetic data and introduces Pareto optimization to evaluate the multi-dimensional performance of the model. The goal is to find multi-dimensional optimal trade-offs rather than a single optimal one. This enhances the model's survival ability in unexpected situations and promotes the development of AI towards reliability, transparency and trustworthiness.

As synthetic data and Ω-Robustness reshape how we anticipate tail risks, one critical question remains:

Can we truly build reliable AI from the uncertainties we seek to simulate?

Works Cited

NVIDIA. “What Is Synthetic Data?” The Official NVIDIA Blog, 8 Dec. 2021, https://blogs.nvidia.com/blog/what-is-synthetic-data/. Accessed 26 Apr. 2025.

“Generative Adversarial Network (GAN).” GeeksforGeeks, 20 Apr. 2023, https://www.geeksforgeeks.org/generative-adversarial-network-gan/. Accessed 26 Apr. 2025.

“Variational Autoencoders (VAE): A Beginner’s Guide.” Spot Intelligence, 27 Dec. 2023, https://spotintelligence.com/2023/12/27/variational-autoencoders-vae/. Accessed 26 Apr. 2025.

“Synthetic Data.” Lyzr AI, https://www.lyzr.ai/glossaries/synthetic-data/?utm_source=chatgpt.com. Accessed 26 Apr. 2025.