ABOUT THIS ISSUE

How this newsletter was synthesized?

Methodology

This newsletter is generated by an AI pipeline (leveraging Anthropic Sonnet 4.5 & Haiku 4.5) that processes the metadata and abstracts of every new arXiv HCI paper from the past week—161 this issue. Each paper is scored on three dimensions: Practice (applicability for practitioners), Research (scientific contribution), and Strategy (industry implications), with scores from 1-5. Papers passing threshold are grouped into topic clusters, and each cluster is summarized to capture what that body of research is exploring.

Selection Criteria

The pipeline builds a curated selection that balances high scores with topic diversity—and deliberately includes at least one 'contrarian' paper that challenges prevailing assumptions. This selection is then analyzed to identify key findings (patterns across multiple papers) and surprises (results that contradict conventional wisdom). A narrative synthesis ties the week's research together under a unifying frame.

Key Themes Discovered

Field Report: ai-interaction

Trust, Calibration, and Behavioral Alignment

This cluster examines how humans calibrate trust and make decisions when collaborating with AI systems. Core questions: When do users rely on AI predictions, and what happens when they shouldn't? Studies reveal systematic failures—automation bias, selective prediction paradoxes, emotional manipulation tactics—alongside interventions that improve calibration through transparency about AI strengths/weaknesses. The work spans clinical diagnosis, group decision-making, and conversational agents, emphasizing that effective human-AI collaboration requires not just accurate AI but behavioral alignment between system design and human judgment.

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