71 citations · 109 across the 11 of their papers we have counts for
11 papers
How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study
Sanjana Gautam, Houjiang Liu, Yujin Choi +1
In the early stages of scientific research, researchers rely on core scholarly judgments to identify relevant literature, assess credible evidence, and determine which directions m…
Who Owns Creativity and Who Does the Work? Trade-offs in LLM-Supported Research Ideation
Houjiang Liu, Yujin Choi, Sanjana Gautam +3
LLM-based agents offer new potential to accelerate science and reshape research work. However, the quality of researcher contributions can vary significantly depending on human abi…
Exploring Multidimensional Checkworthiness: Designing AI-assisted Claim Prioritization for Human Fact-checkers
Houjiang Liu, Jacek Gwizdka, Matthew Lease
Given the volume of potentially false claims online, claim prioritization is essential in allocating limited human resources available for fact-checking. In this study, we perceive…
Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates
Li Shi, Houjiang Liu, Yian Wong +4
Multi-persona debate systems powered by large language models (LLMs) show promise in reducing confirmation bias, which can fuel echo chambers and social polarization. However, empi…
Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
Houjiang Liu, Anubrata Das, Alexander Boltz +4
While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such…
The State of Human-centered NLP Technology for Fact-checking
Anubrata Das, Houjiang Liu, Venelin Kovatchev +1
Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections…
Voices of Workers: Why a Worker-Centered Approach to Crowd Work Is Challenging
Caifan Du, Matthew Lease
How can we better understand the broad, diverse, shifting, and invisible crowd workforce, so that we can better support it? We present findings from online observations and analysi…
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan +9
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the tea…
The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims
Li Shi, Nilavra Bhattacharya, Anubrata Das +2
We conducted a lab-based eye-tracking study to investigate how the interactivity of an AI-powered fact-checking system affects user interactions, such as dwell time, attention, and…
Proceedings of the CSCW 2021 Workshop -- Investigating and Mitigating Biases in Crowdsourced Data
Danula Hettiachchi, Mark Sanderson, Jorge Goncalves +5
This volume contains the position papers presented at CSCW 2021 Workshop - Investigating and Mitigating Biases in Crowdsourced Data, held online on 23rd October 2021, at the 24th A…
Intelligent Topic Selection for Low-Cost Information Retrieval Evaluation: A New Perspective on Deep vs. Shallow Judging
Mucahid Kutlu, Tamer Elsayed, Matthew Lease
While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional poo…