Synthetic data is computer-generated data rather than data coming from real-word records. This data is typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. In the context of AI and ML, synthetic data is often used when real-world data is scarce or unavailable. It can also be used to augment existing datasets to improve the performance of AI/ML models.
There are several methods for generating synthetic data, including generative adversarial networks (GANs), generative AI, variational autoencoders (VAEs), and simulation models. The specific method used will depend on the type of data being generated and the desired outcome. One of the key benefits of synthetic data is that it can be generated in large quantities, allowing AI/ML models to be trained on much larger datasets than would otherwise be possible. This can lead to improved performance, especially in cases where real-world data is limited. Additionally, synthetic data can be generated to match specific characteristics or distributions, making it possible to train models to recognize patterns in data that may be difficult to find in real-world data.
Another benefit of synthetic data is that it can be used to test AI/ML models in controlled conditions, helping to ensure that they are working as intended. For example, synthetic data can be used to test models for bias and fairness, or to evaluate the robustness of models in the face of adversarial attacks. Overall, synthetic data can play a valuable role in the development and deployment of AI/ML models. However, it is important to keep in mind that synthetic data may not perfectly reflect the characteristics of real-world data, so care must be taken when using it to train and evaluate models.
The use of synthetic data raises several ethical issues that are important to consider. Some of the key ethical issues include:
Bias: Synthetic data may be generated to reflect certain biases, either intentionally or unintentionally. This can lead to AI/ML models that are biased in their predictions and decision-making, potentially exacerbating existing inequalities.
Privacy: Synthetic data may be generated using real-world data, which could include sensitive information about individuals. If the synthetic data is not generated and used in a privacy-preserving manner, it could potentially be used to violate people’s privacy rights.
Misrepresentation: Synthetic data may not accurately reflect real-world data, which could lead to AI/ML models that are not representative of the real world. This could result in models that make incorrect predictions or decisions, which could have serious consequences in fields such as healthcare, finance, or criminal justice.
Lack of transparency: The process of generating synthetic data is often complex, and it can be difficult to understand how synthetic data was generated and what assumptions it reflects. This can make it challenging to interpret the results of AI/ML models trained on synthetic data, and to understand the potential limitations and biases of the models.
Responsibility: If AI/ML models trained on synthetic data are used to make decisions that have a significant impact on individuals or society, it can be difficult to determine who is responsible for the decisions made by the model. This raises important questions about accountability and the ethical use of AI/ML technologies.
It is important to carefully consider these ethical issues when using synthetic data and to ensure that synthetic data is generated and used in a responsible and transparent manner. This may involve taking steps to reduce bias and protect privacy, as well as being transparent about the limitations and assumptions of synthetic data and the models trained on it.
[1,2,3] Hittmeir, M., Ekelhart, A., & Mayer, R. (2019, August). On the utility of synthetic data: An empirical evaluation on machine learning tasks. In Proceedings of the 14th International Conference on Availability, Reliability and Security (pp. 1-6).