Comprehensive Investigation
Comprehensive investigations across diverse scientific domains are currently focused on improving the robustness, fairness, and efficiency of machine learning models. Research emphasizes addressing biases in models, particularly concerning race and gender, and enhancing their generalizability across different datasets and applications, often employing techniques like domain adaptation and data augmentation. These efforts are crucial for ensuring the reliability and ethical deployment of AI in various fields, ranging from healthcare and social media analysis to industrial automation and natural language processing. The ultimate goal is to develop more accurate, trustworthy, and equitable AI systems.
Papers
An investigation into the causes of race bias in AI-based cine CMR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink, Sebastien Roujol, Theodore Barfoot, Shaheim Ogbomo-Harmitt, Miaojing Shi, Andrew P. King
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman, Giovanni Iacca