The field of machine learning is moving towards increasing interpretability and explainability of models, with a focus on developing methods that can provide transparent and trustworthy results. This is particularly important in domains such as healthcare, where understanding the decisions made by models is crucial for building trust and ensuring safety. Recent developments have seen the application of machine learning to improve diagnostic consistency and accuracy in medical imaging, as well as the development of new methods for feature attribution and explanation. Notably, researchers have proposed innovative approaches to addressing the challenges of predictive equivalence in decision trees and enhancing the interpretability of rule-based classifiers. These advances have the potential to significantly improve the reliability and transparency of machine learning models, enabling their wider adoption in critical domains. Noteworthy papers include:

• Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values, which presents a new method for estimating probabilistic values with state-of-the-art performance.
• Enhancing interpretability of rule-based classifiers through feature graphs, which introduces a comprehensive framework for estimating feature contributions in rule-based systems.