Synthetic Data and Generation

[Oslo, Norway]

Synthetic data is created by generative AI (GenAI) algorithms, which can be instructed to create bigger, smaller, fairer or richer versions of the original data. Due to how the synthesization process takes place, the data can be augmented to fit certain characteristics. 

Synthetic data can be described as fake data, generated by computer systems but based on real data. Enterprises create artificial data to test software under development and at scale, and to train Machine Learning (ML) models. 

There are 2 types of data:

• Structured, tabular data
• Unstructured, image and video data

However, the generation process of synthetic datasets often harbors implicit issues, especially regarding the fairness and representativeness of data distribution. These issues can affect the performance of models and potentially lead to biases and discriminatory practices in real-world applications.

Synthetic data is the perfect fuel for AI and machine learning (ML) development projects. Synthetic data is created by GenAI algorithms, which can be instructed to create larger, smaller, fairer or richer versions of the original data. 

Because of the way the synthesis process occurs, the data can be enhanced to fit certain characteristics. In a way, synthetic data is like modeling clay for data scientists and data managers. For example, upsampling minority groups in a dataset can improve the performance of ML models. Alternatively, human biases embedded in the raw data can be removed by introducing fairness constraints into the generation process.

Rather than relying on a training set as a basis, synthetic profiles are independently generated, promoting greater diversity and unpredictability in results. This marks a major change as we move away from relying on data obtained from nature or experiments to using artificially created datasets.

In some cases, these new data sets do not just add to but replace traditional methods, and there are concerns that they may be the result of the ability of AI technology to generate data that may not be entirely accurate or reliable.

While synthetic data is proving invaluable in domains where obtaining real-world data is challenging, expensive, or ethically constrained, concerns are growing about potential threats.

Synthetic data is a growing area of research and development in the field of AI and machine learning. Synthetic data generators (SDG) use algorithms to create new data points that preserve the statistical features of the original data.

Some research topics in synthetic data and generation include: 

• Differential privacy: A key concept in synthetic data generation, differential privacy
• protects the privacy of individuals while still allowing synthetic
• data to be used for training and testing machine learning models.
• Generative adversarial networks (GANs): A state-of-the-art deep generative model that can create new synthetic samples that follow the original dataset's distribution.
• Neural networks: Neural networks can learn to reproduce data and generalize beyond it,
• making them well-suited for synthetic data generation.
• Tree-based models: A promising application of machine learning techniques for
• synthesizing data, especially classification and regression trees (CART) models.
• Healthcare: The development of digital twins for healthcare patients, combined
• with machine learning, can help doctors with prescription and minimally invasive procedures.