You can start with standard fairness metrics, but it's not enough. Synthetic data can artificially satisfy those metrics while introducing new, synthetic biases. You need to analyze the data generation process itself, as bias in synthetic data often shifts to conditional probabilities or latent feature relationships rather than raw proportions.
You can start with standard fairness metrics, but it's not enough. Synthetic data can artificially satisfy those metrics while introducing new, synthetic biases. You need to analyze the data generation process itself, as bias in synthetic data often shifts to conditional probabilities or latent feature relationships rather than raw proportions.
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You can start with standard fairness metrics, but it's not enough. Synthetic data can artificially satisfy those metrics while introducing new, synthetic biases. You need to analyze the data generation process itself, as bias in synthetic data often shifts to conditional probabilities or latent feature relationships rather than raw proportions.
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