Fairgen

Founded in 2021 by Samuel Cohen, Fairgen disrupts traditional research and insights processes with cutting-edge generative AI technology.

FairBoost™ - Reliable Synthetic Sample Boosters

With FairBoost, researchers can confidently run synthetic sample boosters that will double the effective sample score of under-represented niches, while provenly reducing margins of error.

How does it work?

Fairgen’s technology trains a deep machine learning model exclusively on the original general audience dataset. Once trained, Fairgen can successfully predict new near-real synthetic responses from niches between 3% to 15% of the entire dataset.

How effective is it? After hundreds of parallel tests between synthetic and real responses, Fairgen proved that its predictions effectively reduce margins of errors, being statistically equivalent to twice the amount of “real” niche responses.

Multiple end customers and research firms have conducted their own parallel testing and set proprietary benchmarks that range from 2x to 3x “boostability” confidence.

“The generative AI technology developed by Fairgen, combined with a rigorous evaluation of its predictions, has the potential to transform survey sampling.”

Emmanuel Candès, Scientific advisor at Fairgen, Director of the Department of Data Sciences and Statistics at Stanford University.

FairCheck™ - Detect and Remove Fraudulent Respondents

Detect unwanted behavior with AI. Let Fairgen detect unwanted behaviors such as speeders, tired respondents, and random and inaccurate open-ended responses.