To use AI for your business, you need large amounts of data - that you know is unbiased. By detecting and correcting feature imbalances in your data, we ensure that the synthetic data you use is unbiased, represents edge-cases and promotes fairness in AI decision-making.
In real-world datasets, specific groups or demographics are almost always under-represented. Unless the data you're using is analyzed and corrected for data imbalances, you will develop services that exclude parts of your customer base, and needs for services will go un-noticed.
By generating synthetic data that is more representative, you will train AI models that are more accurate across your different customer groups.
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.
To correct for data imbalances and to eliminate bias, you need to know the data, you need the technical skills to understand the metrics and algorithms, and you need to understand fairness.
And once you have the synthetic data, where should you use it? Where shouldn’t you use it? How can it be aligned in your current practices? What compliance regulations will you need to follow? How can it inspire new innovations and business?
We will help.
More to read
The landscape for Fair AI is in flux. Here are some resources to help you and your team keep up-to-date:
Articles about fairness metrics and data:
© Fair AI Data
