Measurement System

Measurement systems are crucial for quantifying phenomena across diverse scientific domains, from evaluating the performance of machine learning models to assessing fairness in algorithms and characterizing physical systems. Current research emphasizes developing robust and reliable metrics, often incorporating information-theoretic measures, deep learning architectures (like CNNs), and causal inference techniques to address challenges like bias, instability, and uncertainty in measurements. These advancements are vital for improving the trustworthiness and interpretability of models and systems across various fields, ranging from robotics and AI to social sciences and metrology.