Whether you're researching, training AI models, or creating synthetic data — ensure it is balanced, bias-aware, and transparently generated.
We help organizations detect and reduce bias in structured and synthetic datasets. Our advanced platform is built on research and it highlights intersectional gaps, documents generation methods, and supports transparency from data to deployment.
• Researchers: Ensure your synthetic data aligns with ethical research standards.
• Data stewards: Monitor and report on dataset fairness.
• AI teams: Train models on representative and equitable data.
• Repositories: Offer metadata guidance for synthetic data deposits.
Synthetic data unlocks the power of AI — but it can misrepresent reality. Edge cases are often missed, minority groups underrepresented, and some data points don't make sense at all. These so-called intersectional hallucinations can silently introduce bias.
Our platform identifies both flaws and accurate patterns (fidelities), helping you understand when synthetic data mirrors or distorts reality. With better data, your AI systems can serve everyone — not just the majority.
Fairness isn’t one-size-fits-all. Do you need to consider gender? Geography? Disability? Our tools and expertise help you navigate these complexities with clarity, transparency, and compliance in mind. Bad data leads to bad decisions. We make it easier to analyze and correct dataset imbalances before they impact your business.
© Fair AI Data
