345 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Microsoft (United States)US17 papers
- Microsoft Research Montréal (Canada)10 papers
- Carnegie Mellon UniversityUS4 papers
- Georgia Institute of TechnologyUS4 papers
- University College LondonGB4 papers
- University of Illinois Urbana-ChampaignUS4 papers
- Bellevue Hospital CenterUS3 papers
- McGill UniversityCA2 papers
- Microsoft Research New York City (United States)2 papers
- Microsoft Research (United Kingdom)GB2 papers
- Orange (France)FR2 papers
- Princeton UniversityUS2 papers
12 papers · 1 filter
A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies
Alicia DeVrio, Myra Cheng, Lisa Egede +2
Recent attention to anthropomorphism -- the attribution of human-like qualities to non-human objects or entities -- of language technologies like LLMs has sparked renewed discussio…
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao +2
Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mit…
As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making
Jingshu Li, Yitian Yang, Q. Vera Liao +2
Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence…
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu +2
Widely deployed large language models (LLMs) can produce convincing yet incorrect outputs, potentially misleading users who may rely on them as if they were correct. To reduce such…
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…
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Shreya Chowdhary, Anna Kawakami, Mary L. Gray +3
Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A…
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…
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Upol Ehsan, Koustuv Saha, Munmun De Choudhury +1
Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap--divide between the technical affordances and the social needs. However,…
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…
How Different Groups Prioritize Ethical Values for Responsible AI
Maurice Jakesch, Zana Buçinca, Saleema Amershi +1
Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While…
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…
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…