By 2022, Gartner estimates over 25% of AI training data will be synthetic. Insurance providers, banks, telcos, and other fast-moving industries already prefer synthetic training data. Why? Synthetic data is better than real data when it comes to AI training. AI models need the right data to be able to learn patterns well enough. Synthetic data generation can upsample rare events, resulting in an up to 15% improvement in AI performance. Using upsampled synthetic data leads to better anomaly detection, such as fraud.
The data synthesization process allows for the introduction of fairness constraints. MOSTLY AI's synthetic data generating algorithms performs exceptionally well in bias mitigation. The algorithm generated a fair version of the US Census dataset by narrowing the gender pay gap to 2%. By introducing the same parity correction, the likelihood of Black recidivism was lowered to 1% from 24% in the Compas recidivism dataset. Fair synthetic data is a mission-critical part of ethical AI.
Synthetic data is an important AI governance tool. It provides explainability and model validation through shareable copies of input data. Use representative synthetic data for model documentation and augmented synthetic data to stress test AI models.
The process of creating test data can be an arduous task. It requires a considerable amount of manual work to obtain a dependable subset of your production data. Maintaining referential integrity, business rules, and representative business scenarios is a long journey full of privacy risks.
Having test data that ticks off these boxes helps test engineers to:
Test engineers no longer need to manually configure the business rules or logic in a test data generator. Our AI-powered synthetic data engine learns all of the dataset's features and takes care of the business rules.