Social Bias

Social bias in artificial intelligence, particularly large language models (LLMs) and related architectures like text-to-image generators, is a significant area of research focusing on identifying, measuring, and mitigating the perpetuation of societal prejudices within AI systems. Current efforts concentrate on developing robust bias detection methods, including novel datasets and evaluation metrics, and exploring various mitigation strategies such as adversarial training, counterfactual data augmentation, and prompt engineering techniques to reduce bias amplification. Understanding and addressing these biases is crucial for ensuring fairness, equity, and trustworthiness in AI applications across diverse sectors, impacting both the development of responsible AI and the broader societal implications of AI deployment.

Papers