Model Error

Model error, encompassing inaccuracies and biases in machine learning predictions, is a central challenge hindering the reliable deployment of AI systems. Current research focuses on characterizing and quantifying these errors across diverse model types, including large language models (LLMs) and deep learning architectures for image recognition and time series analysis, with a particular emphasis on understanding how errors disproportionately affect vulnerable user groups and identifying methods for mitigating these issues. Addressing model error is crucial for improving the trustworthiness, fairness, and overall performance of AI systems across various scientific and practical applications.