The field of machine learning is moving towards increased explainability and interpretability of models, with a focus on developing methods that provide insights into the decision-making processes of complex models. This shift is driven by the need for trustworthy and reliable models in sensitive application areas, such as healthcare and finance. Recent developments have highlighted the importance of explainability in various aspects of machine learning, including clustering, time series analysis, and feature engineering. Notable papers in this area include Forest-Guided Clustering, which provides a model-specific explainability method for random forests, and Towards Explainable Deep Clustering for Time Series Data, which outlines research opportunities for improving the interpretability of deep clustering models. Other noteworthy papers include Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting, which demonstrates the use of Shapley Additive Explanations to improve model transparency, and Multi-Hazard Early Warning Systems for Agriculture, which introduces a framework for multi-hazard forecasting with featural-temporal explanations.