The field of artificial intelligence is rapidly advancing, with a growing focus on human-AI collaboration and decision-making. Recent research has highlighted the importance of explainable AI and user-centric personalization in optimizing human-AI collaboration. The integration of AI in various domains, including government and agriculture, has raised concerns about fairness, transparency, and accountability. To address these concerns, researchers are proposing novel approaches, such as the use of blockchain technology to ensure the security and integrity of AI-driven systems. Additionally, there is a growing recognition of the need for user-centered design and participatory approaches in the development of AI systems, particularly in areas such as dementia care and older adult support. Noteworthy papers in this area include: Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions, which investigated the impact of different explanation styles on user decision-making. A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture, which proposed a novel approach to crop forecasting using a combination of IoT, machine learning, and blockchain technologies. Navigating Privacy and Trust: AI Assistants as Social Support for Older Adults, which examined the dynamics of privacy and trust in the adoption of AI assistants by older adults.