The Double-Edged Sword: Understanding Risks and Benefits of Synthetic Data in AI Development

Risks of synthetic data

Synthetic data is playing an important role in advancing AI, but there are a number of potential risks.

Privacy and security risks

Synthetic data is often cited as a means to address privacy concerns, but if used without proper safeguards, it can actually expose personal information. This is a serious issue that can lead to identity theft, discrimination, and more. There are also security and hostile attack risks if synthetic data is used for malicious purposes.

Bias and quality issues

Because synthetic data is often created through generative models trained on existing datasets, it can inherit the biases of the original data. If the original dataset is biased or of low quality, the resulting synthetic data will suffer from the same issues.

Risk of spreading misinformation

In 2024, advances in synthetic data technology could lead to an explosion of manipulated information, misinformation, and disinformation. In particular, synthetic data generated by generative AI models could produce large amounts of fake information that is difficult to distinguish from the real thing.

Regulatory and legal issues

The use of inaccurate synthetic data can create compliance issues and legal risks for organizations, especially in highly regulated industries such as finance and healthcare.

Synthetic data and the “Habsburg AI” phenomenon

One of the most notable risks is the “Habsburg AI” phenomenon, which refers to the “inbreeding” effect that occurs when AI is trained with self-generated data. Similar to the genetic decline of the Habsburg royal family, when an AI system is trained solely on the output of other AIs, it can become a kind of “inbred mutation,” exhibiting exaggerated and bizarre characteristics.

Researchers warn that a gradual degradation of quality can occur when an AI is repeatedly trained on synthetic data that it generates itself. This is also known as ‘model collapse’, where the AI produces increasingly low-quality output.

Synthetic data and genetic diversity

Potential for increased data diversity

Synthetic data can help address the lack of diversity in real-world datasets. In particular, oversampling techniques such as SMOTE can help you avoid the lack of diversity in your data and gain better insights into important traits.

Research has shown that synthetic data can improve diversity in fields such as healthcare data, and using a variety of data sources can increase the diversity of the synthetic datasets created, making them more representative.

Similarities to genetic diversity

Similarities between synthetic data and genetic diversity have been shown in several studies. For example, synthetic derivatives of wheat have been reported to have notable differences in genetic diversity patterns and genetic populations when compared to bread wheat.

Genotypes generated by methods like HAPNEST show better diversity and generalizability compared to other methods, which are essential characteristics when scaling up to large-scale. Rice University scientists also published a study showing that training AI with synthetic AI-generated content leads to destructive “inbreeding”.

The impact of synthetic data diversity in model training

Recent research is exploring the downstream effects of synthetic data diversity in the pre-training and fine-tuning phases, and introducing new diversity measurement approaches. This shows how the diversity of synthetic data can have a significant impact on the performance of the final AI model.

New solutions to prevent model collapse

Recently, researchers have discovered a way to prevent AI models from degrading when training with synthetic data, which could potentially prevent a looming crisis. These new methods are important for avoiding the “Habsburg AI” phenomenon and leveraging the positive aspects of synthetic data while minimizing the risks.

Additional risks of synthetic data

There are additional risks to using synthetic data beyond those already mentioned. Recent research has pointed to the problem of “false confidence” as an important risk when using synthetic data, which is when developers develop an overconfidence in the performance of models trained on synthetic data.

In addition, re-identification risks are also an important issue, which refers to the risk of personal information being exposed when synthetic data models reflect correlations between variables.