FAIRness Approach

FAIRness approaches in machine learning aim to mitigate biases in algorithms, ensuring equitable outcomes across different demographic groups. Current research focuses on extending fairness considerations to temporal contexts, incorporating FAIR data principles for reproducibility, and adapting fairness techniques to distributed learning frameworks like federated learning, particularly in sensitive domains like healthcare. These efforts are crucial for building trustworthy and reliable AI systems, addressing ethical concerns and improving the accuracy and generalizability of models across diverse populations.