The Fairness Mediator framework addresses how LLMs perpetuate social biases by neutralizing stereotype associations during inference.

• Targets spurious correlations between biased concepts and specific social groups
• Prevents amplification of harmful social biases embedded in training data
• Projects model embeddings into unbiased spaces without retraining
• Improves fairness while maintaining model performance

This research is critical for security and ethics in AI as it provides a practical solution to reduce discriminatory outputs that could harm vulnerable groups, ensuring more equitable AI applications across diverse populations.

Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models