In this episode, Paul Tiwald, Head of Research at MOSTLY AI, demonstrates how to use the Fairness feature to ensure statistical parity in synthetic datasets. Using the popular U.S. Census dataset, Paul highlights the disparities in income distribution between males and females and shows how the Fairness feature can balance these distributions effectively.
Through natural conversation with the MOSTLY AI Assistant, Paul prompts the Assistant to identify sensitive columns like sex and target columns like income, then uses statistical parity fairness to create a balanced dataset without disrupting core distributions or correlations. This powerful feature ensures ethical and unbiased synthetic data generation for sensitive scenarios.
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