Synthetic data unlocks the power of AI, but it often misses edge cases and underrepresents minority groups. Worse, structured synthetic data can contain intersectional hallucinations—data points that don't make sense—while also showing fidelities—accurate patterns. You need to know where both occur in your data.
Our advanced software is built on research and it helps identify these issues, ensuring your synthetic data is fair, balanced, and ready for responsible AI decision-making.
In real-world datasets, specific groups are underrepresented. Without correcting these imbalances, your AI models may exclude key customer segments. By generating more representative synthetic data, we help you build AI systems that perform well across diverse groups.
Fairness isn’t just a metric—it’s complex. What kind of fairness does your data need? Should you consider gender, race, or other factors? Do you even have the right data?
Correcting bias requires deep technical skills, fairness expertise, and understanding of real-world compliance. Our tools, developed from critical data studies and machine learning, will guide you to create truly fair synthetic data.
© Fair AI Data
