Social stereotypes and unfair discrimination

Mitigation strategy

1. Employ pre-processing techniques, such as Counterfactual Data Augmentation and targeted data enrichment, to systematically augment training data with counter-stereotypical examples and neutralize spurious correlations, thereby reducing the model's capacity to learn and reproduce harmful social biases. 2. Implement continuous, rigorous bias auditing and measurement across diverse demographic groups using established fairness benchmarks to quantify systemic disparities, followed by the application of in-processing techniques like model fine-tuning or internal mechanism adjustments (e.g., pruning or projection-based corrections) to align the model with fairness desiderata. 3. Deploy runtime bias mitigation strategies, such as Large Language Model-based self-reflection or multi-agent cooperative filtering, to provide real-time, intra-processing adjustment or post-processing correction of model outputs that exhibit discriminatory language or reinforce social stereotypes.