The field of explainable AI and data valuation is rapidly moving towards developing more robust and efficient methods for attributing feature importance and evaluating data contributions. Researchers are focusing on addressing the challenges of misreporting, hyperparameter sensitivity, and scalability in existing methods. New frameworks and algorithms are being proposed to provide more accurate and reliable explanations, such as multi-criteria rank-based aggregation and causally-motivated approaches. Additionally, there is a growing interest in applying Gaussian processes and Shapley values to data valuation and feature attribution. Notable papers in this area include the proposition of a unified framework for provably efficient algorithms to estimate Shapley values, and the introduction of Fast-DataShapley, a one-pass training method for training data valuation. These innovations are expected to significantly advance the field and enable more transparent and trustworthy AI systems.