Latent Bias

Latent bias in artificial intelligence models, encompassing systematic errors stemming from biased training data, is a significant area of research. Current efforts focus on identifying and mitigating these biases in various model architectures, including large language models and diffusion models, using techniques like importance reweighting and bias addition. Understanding and addressing latent bias is crucial for ensuring fairness, reliability, and responsible deployment of AI systems across diverse applications, from computer vision to medical image analysis and yield forecasting. The ultimate goal is to develop methods that produce unbiased and equitable outcomes.