The rise of artificial intelligence has brought about the need for large amounts of data to train and improve machine learning models. However, data scarcity has been a major hindrance for AI developers, leading to the emergence of synthetic data. According to a Gartner study, nearly 60% of all data in AI will be synthetic by 2024.
Synthetic data, artificially generated, offers benefits such as data privacy and increased accuracy of AI models. It is particularly useful in financial, image classification, and computer-vision-based fields such as autonomous vehicles. However, it also comes with its own set of challenges.
One of the main challenges of synthetic data is its reliance on real data for quality. Synthetic data generated from partial and incomplete data might perform worse than real-world data. Real-world data also contains outliers that may be useful for some models.
Synthetic data is generated using algorithms that model the statistical properties of real data.
While it may emulate the distribution and characteristics of the original data, it cannot capture the richness and complexity of the real-world phenomena it represents. This may lead to ML models trained on synthetic data needing more accurate and effectiveness than those trained on real data.
Generating accurate Artificial Intelligence synthetic data requires significant expertise and resources to ensure it is realistic and meaningful. Small errors in the generation process can lead to significant inaccuracies. The data can also be misleading as it needs more variability and diversity.
Ethical challenges arise with synthetic data as well. Bias is present in all datasets, and incorporating more parameters to remove biases may create and amplify further biases. Removing biases from a dataset may also create a ‘quality-gap’ in models, making AI more artificial than it needs to be.
Artificial Intelligence synthetic data is an important tool for AI modeling in the future. It offers benefits such as data privacy and increased accuracy of models. However, there are better solutions to data scarcity, which comes with challenges. Real-world data is highly dynamic, nuanced, and complex, and synthetic data can never fully capture its richness and complexity. It is important to consider the ethical challenges of using synthetic data and use it in conjunction with real-world data to create accurate and effective machine learning models.
