Decision Sciences · Information Systems and Management
A study of 13 researchers informed an AI-augmented dashboard for monitoring long-running passive sensing studies. Tests of the prototype found promise for daily monitoring and decision-making, while highlighting concerns about transparency and researchers’ expectations of AI.
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.
A survey in ACM Computing Surveys examines how large language models could support scientists across five parts of the research process, from literature searches and experiments to peer review. It also describes limits, ethical concerns and risks to research integrity when generative AI is misused.
An analysis of GitHub Copilot use found that open-source projects produced more code and involved more developers. The gains came with longer coordination time, especially for developers on the edges of project communities.
A survey of human-AI collaboration argues that more capable AI systems do not automatically produce reliable partnerships. It identifies human-centered design, governance and attention to high-stakes uses as central to making these systems trustworthy and beneficial.
An open Zenodo working note tracks water use linked to 10 AI data-centre sites and provides code to reproduce its calculations. It finds that closed-loop cooling shifts roughly 92% to 95% of the modelled water footprint to the electricity grid, rather than removing it.
AI systems can generate proof sketches and formal steps, but those outputs often vary in how much evidence they actually carry. This paper proposes Proof Engine Infrastructure, an architecture that links each claim to a checking procedure and composes only earned results into larger proof goals.
