The field of cybersecurity and machine learning is rapidly evolving, with a growing focus on explainability and interpretability. Recent research has explored the use of explainable AI (XAI) for PCB tamper detection and clustering ensemble methods, highlighting the importance of transparency in high-stakes applications. Additionally, there has been significant progress in the development of novel attack methods, such as ultrasonic communication and adversarial attacks on radio waveforms, which pose new challenges for security systems. Notable papers in this area include: There's Waldo, which introduces a novel PCB forensics approach using XAI on impedance signatures. Interpretable Clustering Ensemble, which proposes the first interpretable clustering ensemble algorithm. SATversary, which demonstrates optimized jamming and spoofing attacks on satellite fingerprinting systems. Local MDI+, which provides a novel extension of the MDI+ framework for sample-specific feature importance. Correlation vs causation in Alzheimer's disease, which investigates the relationships among clinical, cognitive, genetic, and biomarker features using correlation analysis and model interpretability techniques.