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We know synthetic data

We know bias

And we know business

Our services


We help you analyze your needs in discovering what your strategy in synthetic data and machine learning should be. 


Data validation and correction projects

With our unique software we engage with you in a project to analyze, test, modify and validate your data sets. With our expertise in machine learning, social science and business we help you with representative data. 


Workshops and trainings

Custom workshops on bias, synthetic data and fairness. We train organizations in how to eliminate bias - in data and in the workplace. Custom workshops on how to hire and retain diverse teams and how you make sure that your data and AI tools are correctly balanced.


What we do in a data project

Fair AI Data's approach in four steps

1. Find it:

What are your data needs. We use bespoke bias metrics to judge the quality of your raw and synthetic data. We create reports about the representation and fairness of your data. We flag for potential concerns with using the data and how to mitigate those. 

2. Fix it:

We deploy bespoke algorithms and technical support that fix existing bias using machine learning processes both in pre-processed data and during the Machine Learning process. 

3. Use it:

We offer bespoke post-processing AI alignment discussions and documentation, including suggestions on how concepts of human-in-the-loop and transparency/explainability could be employed, how to incorporate reminders of where the synthetic data is more/less robust from a bias, fairness perspective, and how generalizable the synthetic data may or may not be. We can also come with bespoke suggestions for the next round of data collection, synthesis, and use from a bias, fairness, alignment perspective.

4. Teach your team:

Custom designed workshops on:

  1. Fairness and bias, combining social science and ML elements
  2. Finding bias in synthetic data – how to do it
  3. Bias in your team - How to hire and retain a diverse team
  4. Tips on dealing with uncertain, qualitative data

Good synthetic data is


What is represented in your data and what is missing?


What did Machine Learning do to the data?


How can the data be used fairly?

Would you like to speak about your synthetic data issues?

We would like to help.