Identifying Bias through Data Auditing

Algorithmic fairness frameworks often begin by auditing training data to uncover gender imbalances or biased representations. By analyzing the distribution of gender-related features and outcomes, these frameworks help reveal systemic biases embedded in data, enabling developers to take corrective actions such as rebalancing or augmenting datasets to promote fairness.