AI requires information to learn from. The results of any chatbot, recommendation system, or fraud detection will be affected by the quality of the data used for training. The difficulty is that real-life data is not always readily available. It may take a long time to gather, expenses can mount up, and privacy laws may restrict the use of the data.
Given these challenges, many organizations are looking to synthetic data as a viable option.
What Is Synthetic Data?
Synthetic data is data that has been created by someone other than the user, device, or business activity. The purpose is to generate information that is like real data, but doesn’t reveal personal or sensitive information.
Now, let’s say a bank created a fraud detection model. It is able to create realistic transaction patterns, which can be used to train the system without compromising customer privacy, rather than using customer transaction records completely.
Why Organizations Are Using It?
Speed is among the reasons that synthetic data has been given attention.
Traditionally, creating a useful dataset requires the accumulation of data, the correction of errors, the assignment of labels to the data, and the quality checks of the data. That process may take weeks or months for the project.
Teams can create a large amount of data much faster with synthetic data. They can also design specific scenarios for testing, allowing developers to be able to test their models to see how they will react to scenarios that may not be seen in the real world.
Privacy Matters
There are strict requirements for handling personal information for organisations in the healthcare, financial, insurance, and regulated sectors.
Synthetic data is a method used for working with realistic data without revealing actual data from customers. It can be created and controlled to enable teams to keep on building AI applications without dealing with a lot of the privacy issues surrounding delicate information.
Filling the Gaps
Real-life data sets may not be complete. Some events occur very infrequently, and so not enough examples are available for training. Other datasets might have imbalances that can impact model performance.
The use of synthetic data can be used to address these issues by generating more examples and scenarios to support the issue. For example, autonomous vehicle developers can simulate rare traffic scenarios or even unusual weather, which would be hard to consistently gather in the real world.
These examples provide AI systems with additional chances to learn from scenarios they may face in the future.
Where do we use it?
The technology is already being used in various industries. Healthcare researchers use synthetic patient information to aid with research without putting private medical records at risk. A financial institution creates some sample transactions to make its fraud detection program better. Insurers set up scenarios to test the risk model and look at suspicious patterns.
In both, the goal is the same: to increase training databases without relying on sensitive information.
Challenges to Consider
Synthetic data is helpful, but can’t replace actual data. However, if the generated data is not truly representative of the real world, the model that results from the AI may not perform well once it is implemented. Quality control is still crucial in the process.
Other concerns are bias. If there are some skewed patterns in the source data, then so will there be in the synthetic data. The organization needs to invest effort in studying the datasets and testing the models often so as to get good results.
In most projects, synthetic data is not a replacement for real data, but rather it is used in conjunction with real data.
Enhancing AI Testing and AI Model Evaluation
The challenge arises when some situations do not occur frequently in real data sets, thus making the testing of an AI model difficult. Synthetic data provides the development team with greater control over the test process, as they can create specific scenarios as needed.
For instance, thousands of patterns of attacks can be developed to test the response of a security model to various attacks. This can be used to gain insight into the model’s performance before deployment and determine its critical points.
Conclusion
Collecting large amounts of real-world data may be a costly process involving infrastructure, storage, labelling, compliance, and more. These expenses can pose a challenge, particularly for companies just starting on their AI journey.
In response to some of these costs, synthetic data can be used to create additional training examples without having to rely on a large collection of data. It does not replace real data, but it can minimise reliance on costly data acquisition procedures and make AI projects more cost-effective.
