497 citations · 1.9k across the 38 of their papers we have counts for
39 papers
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…
Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory
Ziang Xiao, Susu Zhang, Vivian Lai +1
We address a fundamental challenge in Natural Language Generation (NLG) model evaluation -- the design and evaluation of evaluation metrics. Recognizing the limitations of existing…
Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding
Ziang Xiao, Xingdi Yuan, Q. Vera Liao +2
Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when wo…
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…
fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks
Steven Moore, Q. Vera Liao, Hariharan Subramonyam
To design with AI models, user experience (UX) designers must assess the fit between the model and user needs. Based on user research, they need to contextualize the model's behavi…
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…
Selective Explanations: Leveraging Human Input to Align Explainable AI
Vivian Lai, Yiming Zhang, Chacha Chen +2
While a vast collection of explainable AI (XAI) algorithms have been developed in recent years, they are often criticized for significant gaps with how humans produce and consume e…
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…
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Q. Vera Liao, Yunfeng Zhang, Ronny Luss +2
Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to ev…
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…
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Vivian Lai, Samuel Carton, Rajat Bhatnagar +3
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such im…
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…
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai, Chacha Chen, Q. Vera Liao +2
As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and hea…
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Q. Vera Liao, Kush R. Varshney
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners to build XAI applicati…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
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…
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy +4
In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is t…