Synthetic Data Amplifying Existing Bias

When an organization uses large volumes of generated data to expand its training set for a new model version; due to unmitigated control gaps, after several iterations, the model's handling of edge cases visibly deteriorates, outputs become homogeneous, and previously corrected biases reappear, triggering external stakeholder impacts and causing investigation traces the cause to distributional narrowing in the generated data.