Fair AI Data logotype

Frequently asked questions

Why balanced synthetic data?

By generating synthetic data that is more representative, you will train AI models that are more accurate across your different customer groups.

What is fair?

Fairness is more than just a metric. In fact, fairness is more than many different metrics. And algorithms. And concepts. What kind of fairness do you want in your data? What kinds can you get? What kinds do you need?

Even if you know which kind of fair you want to ensure, which protected classes should you be measuring? Which ones are important in your data? Should you be thinking about gender? (“There are more than two, right? But my data only has two?”) Should you be thinking about race? If so, which ones? And how do you define race? Are you even allowed to collect data on race? (sometimes not…) Does a zip-code work as a stand in? This requires a deep understanding of bias and fairness.