Structural Bias

Structural bias in machine learning refers to systematic errors arising from the inherent structure of data or algorithms, leading to unfair or inaccurate predictions. Current research focuses on detecting and mitigating these biases across various domains, including graph neural networks, large language models, and optimization algorithms, employing techniques like fairness regularizers, causal inference, and disentangled representation learning. Understanding and addressing structural bias is crucial for improving the fairness, robustness, and generalizability of machine learning models, impacting their reliability in diverse applications ranging from decision-support systems to natural language processing.