Bias

Mitigation strategy

1. Prioritize data-level interventions by rigorously curating and augmenting training datasets to ensure demographic and cultural representativeness, actively filtering for explicit biases, and utilizing techniques such as Counterfactual Data Augmentation (CDA) to challenge and reduce stereotypical associations. 2. Implement model-level fairness-aware algorithmic adjustments during fine-tuning, such as incorporating explicit fairness constraints, adversarial training, or contrastive training, to prevent the model from over-relying on spurious, biased features for prediction. 3. Establish intra-processing control mechanisms, specifically employing self-diagnosis or self-reflection approaches (e.g., Self-Bias Mitigation in the Loop) where the LLM is prompted to autonomously assess its generated output for potential social biases and iteratively refine the response for a fairer outcome.