Bias mitigation in language models is a multifaceted challenge that requires careful consideration and proactive measures. One approach involves curating training datasets to ensure diversity and representativeness across demographic, cultural, and linguistic dimensions. Additionally, techniques such as data augmentation, adversarial training, and debiasing algorithms can help mitigate biases inherent in training data. Post-processing methods, such as bias-aware evaluation metrics and fairness constraints, further contribute to fostering fairness and equity in language model outputs.
