arXiv AI

Investigating the Impacts of Generative AI on Information Seeking

arXiv AI
Sep 24

Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness

The paper introduces Safety Nudges, a browser-based tool that displays lightweight, in situ flags when a conversational AI exhibits risky behavior such as hallucination or overconfidence. In a two‑week field study with 45 frequent chatbot users, participants reported that the nudges were useful, clear, and minimally disruptive, and most felt more aware of potential AI harms. However, increased awareness did not automatically translate into measurable changes in user behavior, underscoring the need for relevance, calibration, and user control in nudge design.

By Varshini Elangovan, James Wedgwood, Chhavi Yadav, William Agnew, Sauvik Das, Virginia Smith
arXiv AI
Sep 24

How Children Design and Reason about Trustworthy AI Chatbots

The study explores how children design AI chatbots and what they consider trustworthy. Using a custom chatbot-building environment, 115 learners aged 8‑18 created 119 chatbots and adjusted traits such as confidence, transparency, and formality. Findings show younger children equate trust with purpose‑fulfillment, while older children focus on transparent, calibrated design, and that students calibrate academic chatbots to be more formal and transparent than hobby ones.

By Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Catet\'e, Tiffany Barnes, Xiaoyi Tian
arXiv AI
Oct 2

WaLLM -- Understanding Use and Engagement with a General-Purpose LLM on WhatsApp

The paper introduces WaLLM, a general‑purpose large language model chatbot deployed on WhatsApp to investigate how users interact with open‑ended AI in everyday settings. The study finds that health and well‑being queries dominate user interactions, and that proactive communication and communal lists—adapted to WhatsApp’s affordances—enhance engagement and content discovery. These insights inform the design of general‑purpose LLM services on messaging platforms.

By Hiba Eltigani, Rukhshan Haroon, Asli Kocak, Abdullah Bin Faisal, Noah Martin, Fahad Dogar
arXiv AI
Sep 10

Ordinary, Reasonable Chatbots: Do AI Models Track Human Legal Judgments?

The paper investigates whether large language model (LLM) chatbots can emulate human legal judgments of reasonableness. By comparing responses from 26 LLMs to those of human participants across 25 legal scenarios, the study finds that chatbots generally track human answers but tend to produce more homogeneous, government‑ and corporation‑friendly responses and align more closely with white, male, older, and more educated respondents. The authors note that these patterns warrant further systematic research.

By Nirav Patel, Emily Wenger, Christopher Buccafusco
arXiv AI
Jun 2

VET: A Framework for Analyzing AI Discourse

arXiv:2606. 01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public.

By Meredith Ringel Morris