Social Sciences · Safety Research
A paper on Zenodo proposes a control architecture for AI systems conducting research at speeds that could outpace human oversight. It separates human authority from machine-enforced rules governing which system changes may be authorized, checked, committed or reversed.
A paper proposes a control architecture for AI research systems that can modify their own software, training processes or deployment rules. It separates proposing a change, checking its evidence, authorizing it and committing it to protected state.
Two studies examined how ethicists and lay participants judged low-stakes traffic situations shown in text, video and immersive simulations. Drivers’ character had the most consistent influence, while the effects of following rules and avoiding collisions depended more on how the situation was presented.
People who felt more capable and autonomous when using generative AI chatbots also reported stronger intentions to misuse them, according to a study of 736 users. The findings suggest that the psychological benefits of using AI may sometimes have unintended ethical consequences, although the study cannot establish cause and effect.
AI gateways and the Model Context Protocol can manage connections, identities and traffic, but they do not necessarily create evidence that a specific agent action was authorized before it happened. The paper proposes criteria for producing and independently checking that evidence in regulated settings.
A systematic literature review catalogs unresolved technical challenges in developing artificial general intelligence, including limited common sense, unreliable responses and difficulty handling unfamiliar situations. It points to hybrid systems, embodied approaches and stronger reasoning as possible research directions, while emphasizing the need for better safety and verification.
A review finds that current safety certification rules do not map cleanly onto the behavior of large language models. It argues that safety-critical systems should certify the surrounding control system, keeping the language model in a role whose outputs are independently checked.
A review of 121 studies finds that artificial intelligence is changing journalism through automation and human–machine collaboration, but its legal and social rules remain unsettled. Researchers identify disclosure practices, cultural context and copyright as central issues for future study.
