Bias Challenge

Bias in artificial intelligence models, particularly large language models, diffusion models, and vision-language models, is a significant challenge hindering their reliable and ethical deployment. Current research focuses on identifying and mitigating biases through various techniques, including developing new fairness metrics, analyzing the role of embedding spaces and knowledge graphs in bias propagation, and exploring methods like model pruning and counterfactual data augmentation to create more equitable models. Understanding and addressing these biases is crucial for ensuring fairness, preventing discrimination, and building trustworthy AI systems across diverse applications.