The article examines how AI policies implemented by software organisations affect developers, using insights from 19 interviews. It finds that while such policies aim to reduce risks like data leaks and unauthorized use, they can also impede developers if not properly engaged. The authors propose developer‑centric strategies to help managers and decision makers implement effective AI policies.
arXiv:2608.22930v1 Announce Type: new Abstract: Agentic AI is a fascinating new tool for software development. It is a huge step forward compared to "conventional" AI assisted coding, which in turn w...
arXiv:2608. 05173v1 Announce Type: cross Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole.
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and Society, Center for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems.
arXiv:2609.14796v1 Announce Type: new Abstract: The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion a...
The paper examines AI disclosure policies in top computer science venues, finding them to be highly under‑specified. A survey of 109 researchers shows that disclosures are deemed most necessary for research design tasks and when human involvement is low, and it compiles researchers’ expectations for disclosure content. Analysis of 13,867 disclosure statements from EMNLP 2025 and ICLR 2026 reveals a significant mismatch between these expectations and actual practice, such as frequent disclosure of writing assistance despite it being considered less necessary.
arXiv:2606. 05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs.
arXiv:2608. 08022v1 Announce Type: new Abstract: Recent incidents involving Artificial Intelligence (AI) agents, which were reported escaping their containment `unintentionally' to gain unauthorized access, pose looming questions about who or what should be held legally responsible for resultant criminal or negligent damage.
arXiv:2508. 09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies.
arXiv:2411.08881v3 Announce Type: replace-cross Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bi...
We’ve written a policy research paper identifying four strategies that can be used today to improve the likelihood of long-term industry cooperation on safety norms in AI: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. Our analysis shows that industry cooperation on safety will be instrumental in ensuring that AI systems are safe and beneficial, but competitive pressures could lead to a collective action problem, potentially causing AI companies to under-invest in safety.