HCI

Opening — Why this matters now Human–AI decision-making research is quietly facing a credibility problem — and it has little to do with model accuracy, explainability, or alignment. It has everything to do with incentives. As AI systems increasingly assist (or override) human judgment in domains like law, medicine, finance, and content moderation, researchers rely on empirical studies to understand how humans interact with AI advice. These studies, in turn, rely heavily on crowd workers playing the role of decision-makers. Yet one foundational design choice is often treated as an afterthought: how participants are paid. ...

For centuries, reading has meant scrolling—page by page, line by line. But what if reading could mean navigating a tree? TreeReader, a new system from researchers at the University of Toronto and the Vector Institute, challenges the linearity of academic literature. It proposes a reimagined interface: one where large language models (LLMs) summarize each section and paragraph into collapsible nodes in a hierarchical tree, letting readers skim, zoom, and verify with surgical precision. The result is more than a UX tweak—it’s a new cognitive model for how scholars might interact with complex documents in the era of AI. ...