Bias Mitigation

Often AI outputs can be unfair and prejudiced.  Sometimes intentionally, sometimes unintentionally.

There are many possible causes for this, including erroneous assumptions in the Machine Learning (ML) methodology, incorrectly annotated data, or selection bias in the ML training data itself.

We work with your organization to design and implement strategies to identify and mitigate bias in data annotation, machine learning training, and AI algorithms, ensuring fairness and equitable outcomes across diverse user groups.