The field of artificial intelligence is moving towards a greater emphasis on human-AI collaboration, with a focus on understanding the complex interactions between humans and AI systems. Recent research has highlighted the importance of considering the social and psychological factors that influence human trust in AI, including the impact of bias, social norms, and individual attitudes towards AI. Studies have shown that humans are vulnerable to bias in AI-generated suggestions, and that individual attitudes towards AI can significantly impact performance in human-AI collaboration tasks. Furthermore, research has demonstrated the need for formal verification and quality assurance measures to ensure the reliability and trustworthiness of AI systems, particularly in high-stakes applications such as finance and healthcare. Noteworthy papers in this area include: Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case, which employed formal verification to ensure the reliability of a neural network model, and Bias in the Loop: How Humans Evaluate AI-Generated Suggestions, which examined the psychological factors that determine the success or failure of human-AI collaboration. No Thoughts Just AI: Biased LLM Recommendations Limit Human Agency in Resume Screening is also notable for its investigation into the impact of AI bias on human decision-making in hiring scenarios.