The field of artificial intelligence is moving towards a greater emphasis on safety and certification, with a focus on developing practical schemes for ensuring that AI systems are safe, lawful, and socially acceptable. This is being driven by the increasing adoption of AI in safety-critical applications, and the need for transparent and reproducible evidence of model quality in real-world settings. Researchers are exploring new approaches to certifying AI systems, including the development of audit catalogs and methodologies for assessing and certifying machine learning systems. There is also a growing recognition of the importance of human oversight in AI systems, and the need to secure this oversight against potential attacks. Additionally, probabilistic model checking is being applied to a wide range of problems, and monitoring of machine learning systems is becoming increasingly important in dynamic production environments. Noteworthy papers include: Secure and Certifiable AI Systems, which presents a framework for assessing and certifying machine learning systems, and Secure Human Oversight of AI, which explores the attack surface of human oversight and outlines hardening strategies to mitigate these risks.