Systematic Bias

Systematic bias, the consistent deviation of results from true values, is a pervasive problem across diverse scientific fields, hindering accurate modeling and fair decision-making. Current research focuses on identifying and mitigating these biases in various contexts, including machine learning models (e.g., through bias correction layers in autoencoders and constrained optimization approaches), natural language processing (e.g., by developing bias-agnostic methods and augmenting datasets), and even human judgment in tasks like peer review and LLM evaluation. Addressing systematic bias is crucial for improving the reliability and validity of scientific findings and ensuring equitable outcomes in applications ranging from healthcare to criminal justice.