Erasing social groups

Mitigation strategy

1. Mandate the use of diverse and representative training data sets, actively employing reweighting, resampling, or targeted data augmentation to ensure equitable coverage of all relevant social and intersectional groups, thereby directly counteracting systematic under-representation and erasure. 2. Integrate rigorous, group-specific fairness metrics and representational harm measurement techniques (e.g., for erasing or vagueness) into the model development lifecycle to quantitatively assess performance disparities and identify algorithmic choices that fail to acknowledge the identity or causes of specific groups in the output. 3. Establish a continuous AI governance strategy that includes regular, repeatable risk assessments and auditing of model behavior in deployment to detect bias drift and ensure accountability, with detailed documentation of all human and algorithmic decisions related to training data sourcing and output policies.