Search Results - "Liao, Q. Vera"

Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions by Vasconcelos, Helena, Bansal, Gagan, Fourney, Adam, Liao, Q. Vera, Vaughan, Jennifer Wortman

“…Large-scale generative models enabled the development of AI-powered code completion tools to assist programmers in writing code. However, much like other…”
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Questioning the AI: Informing Design Practices for Explainable AI User Experiences by Liao, Q. Vera, Gruen, Daniel, Miller, Sarah

“…A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate…”
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Seamful XAI: Operationalizing Seamful Design in Explainable AI by Ehsan, Upol, Liao, Q. Vera, Passi, Samir, Riedl, Mark O, DaumeIII, Hal

“…ACM CSCW 2024 Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make…”
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"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust by Kim, Sunnie S. Y, Liao, Q. Vera, Vorvoreanu, Mihaela, Ballard, Stephanie, Vaughan, Jennifer Wortman

“…Widely deployed large language models (LLMs) can produce convincing yet incorrect outputs, potentially misleading users who may rely on them as if they were…”
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Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding by Xiao, Ziang, Yuan, Xingdi, Liao, Q. Vera, Abdelghani, Rania, Oudeyer, Pierre-Yves

“…Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive,…”
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The Who in XAI: How AI Background Shapes Perceptions of AI Explanations by Ehsan, Upol, Passi, Samir, Liao, Q. Vera, Chan, Larry, Lee, I-Hsiang, Muller, Michael, Riedl, Mark O

“…ACM CHI 2024 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…”
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Designing for Responsible Trust in AI Systems: A Communication Perspective by Liao, Q. Vera, Sundar, S. Shyam

“…Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how…”
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Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML by Narkar, Shweta, Zhang, Yunfeng, Liao, Q. Vera, Wang, Dakuo, Weisz, Justin D

“…Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and…”
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Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models by Park, Soya, Wang, April, Kawas, Ban, Liao, Q. Vera, Piorkowski, David, Danilevsky, Marina

“…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…”
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Expanding Explainability: Towards Social Transparency in AI systems by Ehsan, Upol, Liao, Q. Vera, Muller, Michael, Riedl, Mark O, Weisz, Justin D

“…As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable…”
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Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making by Zhang, Yunfeng, Liao, Q. Vera, Bellamy, Rachel K. E

“…Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable,…”
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Human-Centered Responsible Artificial Intelligence: Current & Future Trends by Tahaei, Mohammad, Constantinides, Marios, Quercia, Daniele, Kennedy, Sean, Muller, Michael, Stumpf, Simone, Liao, Q. Vera, Baeza-Yates, Ricardo, Aroyo, Lora, Holbrook, Jess, Luger, Ewa, Madaio, Michael, Blumenfeld, Ilana Golbin, De-Arteaga, Maria, Vitak, Jessica, Olteanu, Alexandra

“…In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research…”
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What’s in it for me? Self-serving versus other-oriented framing in messages advocating use of prosocial peer-to-peer services by Vaish, Rajan, Liao, Q. Vera, Bellotti, Victoria

“…•Senders generally prioritized other-oriented motives.•Senders paid attention to the service in question when deciding how to motivate recipients to join…”
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Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions by Xiao, Ziang, Zhou, Michelle X, Liao, Q. Vera, Mark, Gloria, Chi, Changyan, Chen, Wenxi, Yang, Huahai

“…The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended…”
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Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment by Dodge, Jonathan, Liao, Q. Vera, Zhang, Yunfeng, Bellamy, Rachel K. E, Dugan, Casey

“…Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness…”
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Rethinking Model Evaluation as Narrowing the Socio-Technical Gap by Liao, Q. Vera, Xiao, Ziang

“…The recent development of generative and large language models (LLMs) poses new challenges for model evaluation that the research community and industry are…”
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Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking by Sharma, Nikhil, Liao, Q. Vera, Xiao, Ziang

“…Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many…”
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AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap by Liao, Q. Vera, Vaughan, Jennifer Wortman

“…The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at…”
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fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks by Moore, Steven, Liao, Q. Vera, Subramonyam, Hariharan

“…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…”
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Human-Centered Explainable AI (XAI): From Algorithms to User Experiences by Liao, Q. Vera, Varshney, Kush R

“…In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners…”
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