497 citations · 1.9k across the 38 of their papers we have counts for
21 papers · 1 filter
AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources
Dalia Ali, Maria José Rodríguez Velázquez, Manoel Horta Ribeiro +2
Generative AI (GenAI) deployment in the workplace is accelerating rapidly. Nevertheless, questions of who adopts, who benefits, and who is left behind and why are still understudie…
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap
Q. Vera Liao, Ziang Xiao
The recent development of generative large language models (LLMs) poses new challenges for model evaluation that the research community and industry have been grappling with. While…
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao, Jennifer Wortman Vaughan
The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached…
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
Haotian Li, Yun Wang, Q. Vera Liao +1
Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data wo…
Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders
Anna Kawakami, Shreya Chowdhary, Shamsi T. Iqbal +4
With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing corporate interest in using passive sensi…
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang +1
Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for AI-powered systems remains challenging. UX designers face hurdles understanding AI t…
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
Mohammad Tahaei, Marios Constantinides, Daniele Quercia +13
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research communities may use diffe…
Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access
Ziang Xiao, Q. Vera Liao, Michelle X. Zhou +2
During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health plan…
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan +1
AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on th…
Seamful XAI: Operationalizing Seamful Design in Explainable AI
Upol Ehsan, Q. Vera Liao, Samir Passi +2
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make the user experience seamless, hiding…
Designing for Responsible Trust in AI Systems: A Communication Perspective
Q. Vera Liao, S. Shyam Sundar
Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust…
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun, Q. Vera Liao, Michael Muller +4
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative mo…
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan, Samir Passi, Q. Vera Liao +4
Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-me…
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Shweta Narkar, Yunfeng Zhang, Q. Vera Liao +2
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.…
Question-Driven Design Process for Explainable AI User Experiences
Q. Vera Liao, Milena Pribić, Jaesik Han +2
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI)…
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Wang, Ban Kawas +3
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer val…
Expanding Explainability: Towards Social Transparency in AI systems
Upol Ehsan, Q. Vera Liao, Michael Muller +2
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in h…
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +1
Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effect…
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang +2
The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to th…
Enabling Value Sensitive AI Systems through Participatory Design Fictions
Q. Vera Liao, Michael Muller
Two general routes have been followed to develop artificial agents that are sensitive to human values---a top-down approach to encode values into the agents, and a bottom-up approa…
Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions
Ziang Xiao, Michelle X. Zhou, Q. Vera Liao +4
The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user…