The field of artificial intelligence is moving towards a more responsible and ethical development and deployment of AI systems. Researchers are focusing on addressing the technical, organizational, and institutional challenges that hinder the adoption of AI in various sectors, particularly in the public sector. There is a growing emphasis on developing frameworks and tools that prioritize fairness, transparency, and accountability in AI systems. Additionally, the importance of inclusive innovation and its potential to drive economic returns is being recognized. Noteworthy papers in this area include: Responsible AI Adoption in the Public Sector, which develops a taxonomy of data-related challenges to responsible AI adoption in government. Beyond Ethics, which introduces the concept of the 'inclusive innovation dividend' and demonstrates how inclusive healthcare AI development can create business value. Measuring What Matters, which examines how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Making Power Explicable in AI, which presents a diagnosis and interventional recommendations for addressing the problem of ineffective implementation of AI ethics. AI Alignment Strategies from a Risk Perspective, which analyzes the failure modes of alignment techniques and discusses the implications for understanding the current level of risk. Artificial Intelligence Virtual Cells, which proposes a model-agnostic Cell-State Latent perspective that organizes learning via an operator grammar. Evidence Without Injustice, which introduces a new counterfactual test for fair algorithms. Implementation of AI in Precision Medicine, which provides a scoping review of literature on the implementation of AI in precision medicine. Machine Learning and Public Health, which presents a systematic literature review of algorithmic bias identification, discussion, and reporting in Dutch public health ML research.