The field of artificial intelligence is moving towards the development of transparent and trustworthy models, particularly in high-stakes domains such as finance and healthcare. Researchers are exploring innovative approaches to explainability, including the use of interpretable neural networks, local explainability methods, and human-centered design. These efforts aim to provide insights into the decision-making processes of AI models, fostering trust and accountability in their outputs. Noteworthy papers in this area include: IQNN-CS, which introduces an interpretable quantum neural network framework for credit risk classification. Preliminary Quantitative Study on Explainability and Trust in AI Systems, which investigates the relationship between explainability and user trust in AI systems. Explainability of Large Language Models, which reviews local explainability and mechanistic interpretability approaches for large language models. Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop, which tests a practitioner-in-the-loop workflow that pairs transparent decision-tree models with large language models. Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions, which presents a human-centered multi-agent system for anomaly detection in digital asset transactions.