Decision Sciences · Information Systems and Management
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.
