"Synthetic data is the perfect input into machine learning and AI" - read the EU report on synthetic data
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Fair synthetic data for ethical AI

Synthetic data is not just a privacy-enhancing technology. Synthetic data generation is a data augmentation tool. The synthesis process is capable of introducing different constraints, such as fairness. The result is fair synthetic data, devoid of bias. We like to think about fair synthetic data as a reflection of the world as we would like to see it, not as it is right now.

Why is bias in AI a problem?

AI algorithms are very good at amplifying things. When an AI system is trained on biased data, a business can end up with thousands if not millions of biased decisions. The damage is not only to consumers subjected to biased decisions but also to a company's reputation and profit. What makes an AI algorithm biased? The answer lies in the biased training data. There are lots of cautionary tales out there, already demonstrating how biased historic data can create biased algorithms.

10 reasons for bias

From insufficient training data to model drift and faulty fairness definitions, bias takes many shapes and forms. If you develop machine learning models, dedicate time and effort to exploring the ethical aspects of your algorithms, such as bias. Here is a good way to start the exploration. Read through all ten reasons for bias and investigate your machine learning algorithm for each.

How to define fairness?

The next step in the journey to ethically designed machine learning algorithms is to define fairness. What is fair? Equal treatment of everyone? Does that lead to equal opportunity? What is the mathematical definition of fairness? Spoiler alert: there are a few. It’s time to get deeper into the exciting and important topic of fairness and learn about all the options for algorithmic fairness.

What is fair synthetic data?

Bias-corrected synthetic data can address fairness issues efficiently. Using synthetization, you can fix embedded injustices right at the heart of the problem, within the data. Once you have a fairness definition, like statistical parity, you can synthesize fair versions of your data. The result is accurate, privacy-safe, and most importantly fair synthetic data. Fair synthetic data is better than real data for training machine learning models.

How to generate fair synthetic data?

It’s time to get hands-on and generate your fair synthetic dataset. In the Community Version of MOSTLY AI’s synthetic data platform, you can use the world's most advanced synthetic data generator for free! As to the know-how, read the final part of the Fairness Series to learn how to introduce the fairness constraint during synthetic data generation.

Fair synthetic data for ethical AI

Since we introduced the concept of fair synthetic data, it has become one of the central topics in ethical AI discussions. Forbes published an extensive analysis of the potential of fair synthetic data. We had the honor of presenting the concept at leading AI conferences. After many discussions, we expect to see regulations demanding fairness at the data-level. The pace is accelerating for algorithmic fairness and there is more to come. 

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