10 citations · 19 across the 15 of their papers we have counts for
18 papers
How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process
Yu Fu, Jiawei Zhou, Sichen Jin +3
Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader mean…
Follow the Rules (or Not): Community Norms and AI-Generated Support in Online Health Communities
Shravika Mittal, Erin Kasson, Layna Paraboschi +5
Generative AI (GenAI) is increasingly being integrated into the online ecosystem, including online health communities (OHCs), where people with diverse health conditions exchange s…
Responsible Evaluation of AI for Mental Health
Hiba Arnaout, Anmol Goel, H. Andrew Schwartz +13
Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned wi…
What About the Scene with the Hitler Reference? HAUNT: A Framework to Probe LLMs' Self-consistency Via Adversarial Nudge
Arka Dutta, Sujan Dutta, Rijul Magu +3
Hallucinations pose a critical challenge to the real-world deployment of large language models (LLMs) in high-stakes domains. In this paper, we present a framework for stress testi…
Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation
Navreet Kaur, Hoda Ayad, Hayoung Jung +3
Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed -- creates specific needs for an appropr…
Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development
Sanjana Gautam, Mohit Chandra, Ankolika De +3
Lived experiences fundamentally shape how individuals interact with AI systems, influencing perceptions of safety, trust, and usability. While prior research has focused on develop…
MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
Hayoung Jung, Shravika Mittal, Ananya Aatreya +3
Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains…
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations
Mohit Chandra, Siddharth Sriraman, Harneet Singh Khanuja +2
Limited access to mental healthcare, extended wait times, and increasing capabilities of Large Language Models (LLMs) has led individuals to turn to LLMs for fulfilling their menta…
Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities
Shravika Mittal, Darshi Shah, Shin Won Do +3
Widespread stigma, both in the offline and online spaces, acts as a barrier to harm reduction efforts in the context of opioid use disorder (OUD). This stigma is prominently direct…
Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack Narratives Targeting Mental Health Groups
Rijul Magu, Arka Dutta, Sean Kim +2
Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk popu…
A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models
Gaurav Verma, Jiawei Zhou, Mohit Chandra +2
Large artificial intelligence (AI) models have garnered significant attention for their remarkable, often "superhuman", performance on standardized benchmarks. However, when these…
From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
Mohit Chandra, Suchismita Naik, Denae Ford +8
Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to un…
Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models
Luhang Sun, Varsha Pendyala, Yun-Shiuan Chuang +6
This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-…
Do Large Language Models Align with Core Mental Health Counseling Competencies?
Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong +8
The rapid evolution of Large Language Models (LLMs) presents a promising solution to the global shortage of mental health professionals. However, their alignment with essential cou…
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use
Mohit Chandra, Siddharth Sriraman, Gaurav Verma +5
Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. With healthcare systems and online communities fa…
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
Vibhor Agarwal, Yiqiao Jin, Mohit Chandra +3
Large language models (LLMs) are starting to complement traditional information seeking mechanisms such as web search. LLM-powered chatbots like ChatGPT are gaining prominence amon…
A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
Gaurav Verma, Rynaa Grover, Jiawei Zhou +4
Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crim…
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Yiqiao Jin, Mohit Chandra, Gaurav Verma +3
Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like heal…