About

Karrie G. Karahalios is a professor in the Department of Computer Science at the University of Illinois, with additional affiliations as an affiliate professor in the Unit for Criticism & Interpretive Theory, Electrical and Computer Engineering, and the School of Library and Information Science at the same university. She is also the co-director and founder of the Center for People and Infrastructures at the Coordinated Science Laboratory. Her educational background includes a Ph.D. in Media Arts and Sciences from the Massachusetts Institute of Technology, obtained in 2004. Karahalios's research focuses on understanding social and ethical aspects of computing, including investigating bias in social media and web search, analyzing emotional arousal in children with neurodevelopmental impairments, and diagnosing ethical harm in software algorithms. Her work often explores the societal impacts of technology, algorithmic bias, and the visualization of complex data to facilitate better human-computer interaction. She has contributed to the academic community through numerous publications in journals and conference proceedings, emphasizing her role as a leading researcher in her field.

Research topics

• Computer Science
• Sociology
• Engineering
• Internet privacy
• Accounting
• Business
• Visual arts
• Medical education
• Algorithm
• World Wide Web
• Epistemology
• Software engineering
• Data science
• Psychology
• Mathematics education
• Transport engineering
• Finance
• Human–computer interaction
• Art
• Multimedia

Selected publications

• Towards Open Benchmarks for Human Flourishing with AI: Report of the October '25 Workshop and Next Steps

  2026-01-01

  articleOpen access
  PublisherDOI

• Supporting Learners' Use of Imperfect Generative Pedagogical Chatbots: The Role of Chatbot Response Uncertainty and Reduced Verbosity

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Generative chatbots promise to scale personalized learning. Most publicly available generative chatbots are designed to provide confident and eloquent responses by default, even when hallucinating. Prior work has observed that learners using such chatbots often engage shallowly and fail to detect chatbot errors due to overtrust, cognitive overload, and prioritization of short-term gains. To address these challenges, this work examines two chatbot design options in a STEM learning context: introducing verbal uncertainty and reducing response verbosity. Using Bayesian causal inference and thematic analysis in a quasi-experimental setting, we found that a less verbose chatbot improved detection of errors with logical fallacies, but did not increase the use of alternative resources. A chatbot that always expressed uncertainty reduced the adoption of incorrect chatbot responses, but had mixed effects on learning outcomes, suggesting the need to increase signal credibility and maintain learners’ engagement in the learning process despite chatbot disuse.

  PublisherDOI

• Control in Context: How Smart Home Users Navigate Proxy-based Schemes

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  A homeowner controls their smart home devices along a spectrum of approaches, ranging from physical device control to various proxy-based control modalities. This paper studies how and why users move along this spectrum in their day-to-day lives, building upon existing research that focused only on specific interactions. We surveyed smart home owners (N = 43 users), and conducted follow-up interviews with a subset of the survey participants (N = 8). Our studies allow us to both distill specific contexts and experiences of smart home owners as they navigate the control spectrum, as well as to describe how their experiences (both positive and negative) shape their tendencies to control devices in a particular way. These insights lead us to propose practical implications for designers and researchers of smart home management systems, including the need to support flexible control scheme transitions, reduce switching costs, and account for temporal and spatial heterogeneity in the evaluation and design of control systems.

  PublisherDOI

• The Global Impact of Generative AI on the HCI Landscape: International Perspectives on HCI Education, Industry Dynamics, and Funding Considerations

  2026-04-13 · 1 citations

  articleOpen access

  In recent years, we have witnessed a boom of AI-related research from both academia and industry at CHI. Built upon these ongoing conversations and a recent panel at CSCW 2025, this panel aims to promote community-wide discussions that reflect on generative AI’s multidimensional impact on the global HCI landscape beyond specific research agendas or directions. In particular, rather than discussing such impact at the regional or even national level, we will highlight international perspectives on AI’s impact on HCI education, industry dynamics, and funding considerations across various cultures and regions. Featuring a diverse group of panelists, including academic leaders in HCI education and industry experts from various regions, this panel aims to foster collective reflection at CHI on key questions crucial to sustaining the future of HCI as an international community.

  PublisherDOI

• V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning

  ArXiv.org · 2025-07-08

  preprintOpen accessSenior author

  Visual feedback speeds up learners' improvement of pronunciation in a second language. The visual combined with audio allows speakers to see sounds and differences in pronunciation that they are unable to hear. Prior studies have tested different visual methods for improving pronunciation, however, we do not have conclusive understanding of what aspects of the visualizations contributed to improvements. Based on previous work, we created V(is)owel, an interactive vowel chart. Vowel charts provide actionable feedback by directly mapping physical tongue movement onto a chart. We compared V(is)owel with an auditory-only method to explore how learners parse visual and auditory feedback to understand how and why visual feedback is effective for pronunciation improvement. The findings suggest that designers should include explicit anatomical feedback that directly maps onto physical movement for phonetically untrained learners. Furthermore, visual feedback has the potential to motivate more practice since all eight of the participants cited using the visuals as a goal with V(is)owel versus relying on their own judgment with audio alone. Their statements are backed up by all participants practicing words with V(is)owel more than with audio-only. Our results indicate that V(is)owel is effective at providing actionable feedback, demonstrating the potential of visual feedback methods in second language learning.

  PublisherOA PDFDOI

• Organize, Then Vote: Exploring Cognitive Load in Quadratic Survey Interfaces

  2025-04-24 · 2 citations

  preprintOpen access

  Quadratic Surveys (QSs) elicit more accurate preferences than traditional methods like Likert-scale surveys. However, the cognitive load associated with QSs has hindered their adoption in digital surveys for collective decision-making. We introduce a two-phase "organize-then-vote" QS to reduce cognitive load. As interface design significantly impacts survey results and accuracy, our design scaffolds survey takers' decision-making while managing the cognitive load imposed by QS. In a 2x2 between-subject in-lab study on public resource allotment, we compared our interface with a traditional text interface across a QS with 6 (short) and 24 (long) options. Two-phase interface participants spent more time per option and exhibited shorter voting edit distances. We qualitatively observed shifts in cognitive effort from mechanical operations to constructing more comprehensive preferences. We conclude that this interface promoted deeper engagement, potentially reducing satisficing behaviors caused by cognitive overload in longer QSs. This research clarifies how human-centered design improves preference elicitation tools for collective decision-making.

  PublisherDOI

• Budget, Cost, or Both? An Empirical Exploration of Mechanisms in Quadratic Surveys

  2025-07-29

  articleOpen access
  PublisherDOI

• Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen accessSenior author

  Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.

  PublisherDOI

• From Sociotechnical Gaps to Solutions: Designing AI Tools with Parents to Address Special Education Advocacy Barriers in IEP Processes

  2025-07-04

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Placebo Effect of Control Settings in Feeds Are Not Always Strong

  2025-04-24

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• CHS: Medium: Collaborative Research: Beyond the Black Box: Understanding and Designing for User Expectations of Algorithmic Media

  NSF · $846k · 2016–2021

• CAREER: Enhancing Vocal Communication using Graphical Social Cues

  NSF · $500k · 2007–2013

Frequent coauthors

• Aditya Parameswaran

  28 shared

• Motahhare Eslami

  Carnegie Mellon University

  25 shared

• Christian Sandvig

  19 shared

• Ha‐Kyung Kong

  Rochester Institute of Technology

  18 shared

• Joshua Hailpern

  Hewlett-Packard (United States)

  17 shared

• Éric Gilbert

  17 shared

• Tony Bergstrom

  University of Illinois Urbana-Champaign

  15 shared

• Tarique Siddiqui

  Microsoft (United States)

  13 shared

Education

• PhD

  Massachusetts Institute of Technology

  2004

Awards & honors

• Celebration of Excellence 2023
• Celebration of Excellence 2022
• Celebration of Excellence 2021
• Celebration of Excellence 2024
• Celebration of Excellence 2025