The field of machine learning is currently moving towards addressing issues of fairness and class balance in various applications. Researchers are exploring innovative methods to ensure that models are fair and unbiased, and that they can effectively handle imbalanced datasets. One of the key directions is the development of post-processing algorithms that can be used to adjust the weights of neural network models to satisfy fairness constraints. Another area of focus is the use of explainable techniques, such as singular value decomposition, to analyze and improve the fairness of models. Additionally, researchers are investigating the use of dynamical inter-class separability metrics and sub-clustering contrastive learning approaches to improve the accuracy of tail classes in long-tailed datasets. Noteworthy papers include:

• Post-processing for Fair Regression via Explainable SVD, which proposes a novel algorithm for training fair neural network regression models.
• Fairness in Machine Learning-based Hand Load Estimation, which develops a fair predictive model for hand load estimation that leverages a Variational Autoencoder with feature disentanglement.
• Uncovering Fairness through Data Complexity as an Early Indicator, which investigates the relationship between disparities in classification complexity and fairness in machine learning applications.