About

I am a Distinguished McKnight University Professor of Computer Science & Engineering at The University of Minnesota. As of 2025, I serve as Department Head for Computer Science & Engineering. I also co-lead the GroupLens Research Lab. My research areas are social computing, human-computer interaction, and intelligent systems. I focus on issues including detecting and addressing bias, methods for doing ethical research with online communities, and integrating human and machine intelligence in systems like Wikipedia. I have interface design expertise in contexts including mobile and location-aware systems, and web search and information management.

Research topics

• Computer Science
• Sociology
• Social Science
• Artificial Intelligence
• World Wide Web
• Engineering
• Knowledge management
• Human–computer interaction
• Programming language
• Psychology
• Economics
• Cognitive psychology
• Anthropology
• Data science
• Microeconomics
• Social psychology
• Mathematics
• Neuroscience

Selected publications

• Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

  arXiv (Cornell University) · 2026-02-17

  preprintOpen access

  Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

  PublisherDOI

• Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

  2026-04-13 · 1 citations

  articleOpen access

  Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study (n = 28), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm (n = 10) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

  PublisherDOI

• Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

  arXiv (Cornell University) · 2026-01-16

  preprintOpen access

  Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study ($n=28$), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm ($n=10$) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

  PublisherDOI

• Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

  2026-04-13 · 1 citations

  articleOpen access

  Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

  PublisherOA PDFDOI

• Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

  ArXiv.org · 2026-01-16

  articleOpen access

  Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study ($n=28$), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm ($n=10$) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

  PublisherOA PDF

• Observe, Ask, Intervene: Designing AI Agents for More Inclusive Meetings

  2025-04-24 · 6 citations

  preprintOpen access

  Video conferencing meetings are more effective when they are inclusive, but inclusion often hinges on meeting leaders' and/or co-facilitators' practices. AI systems can be designed to improve meeting inclusion at scale by moderating negative meeting behaviors and supporting meeting leaders. We explored this design space by conducting $9$ user-centered ideation sessions, instantiating design insights in a prototype ``virtual co-host'' system, and testing the system in a formative exploratory lab study ($n=68$ across $12$ groups, $18$ interviews). We found that ideation session participants wanted AI agents to ask questions before intervening, which we formalized as the ``Observe, Ask, Intervene'' (OAI) framework. Participants who used our prototype preferred OAI over fully autonomous intervention, but rationalized away the virtual co-host's critical feedback. From these findings, we derive guidelines for designing AI agents to influence behavior and mediate group work. We also contribute methodological and design guidelines specific to mitigating inequitable meeting participation.

  PublisherOA PDFDOI

• Peer Recommendation Interventions for Health-related Social Support: a Feasibility Assessment

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 2 citations

  article

  Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers—a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge.org received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.

  PublisherDOI

• Beyond the Individual: A Community-Engaged Framework for Ethical Online Community Research

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16 · 2 citations

  article

  Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community trust and increased debate in the social computing research community on the ethics of online community research, this paper investigates community-level harms and benefits of research. Through 9 participatory-inspired workshops with four critical online communities (Wikipedia, InTheRooms, CaringBridge, and r/AskHistorians), we found researchers should engage more directly with communities' primary purpose by rationalizing their methods and contributions in the context of community goals to equalize the beneficiaries of community research. To facilitate deeper alignment of these expectations, we present the FACTORS (Functions for Action with Communities: Teaching, Overseeing, Reciprocating, and Sustaining) framework for ethical online community research. Finally, we reflect on our findings by providing implications for researchers and online communities to identify and implement functions for navigating community-level harms and benefits.

  PublisherDOI

• LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities

  ArXiv.org · 2025-09-09

  preprintOpen accessSenior author

  Large language models (LLMs) are reshaping knowledge production as community members increasingly incorporate them into their contribution workflows. However, participating in knowledge communities involves more than just contributing content - it is also a deeply social process. While communities must carefully consider appropriate and responsible LLM integration, the absence of concrete norms has left individual editors to experiment and navigate LLM use on their own. Understanding how LLMs influence community participation is therefore critical in shaping future norms and supporting effective adoption. To address this gap, we investigated Wikipedia, one of the largest knowledge production communities, to understand 1) how LLMs influence the ways editors contribute content, 2) what strategies editors leverage to align LLM outputs with community norms, and 3) how other editors in the community respond to LLM-assisted contributions. Through interviews with 16 Wikipedia editors who had used LLMs for their edits, we found that 1) LLMs affected the content contributions for experienced and new editors differently; 2) aligning LLM outputs with community norms required tacit knowledge that often challenged newcomers; and 3) as a result, other editors responded to LLM-assisted edits differently depending on the editors' expertise level. Based on these findings, we challenge existing models of newcomer involvement and propose design implications for LLMs that support community engagement through scaffolding, teaching, and context awareness.

  PublisherOA PDFDOI

• ORES-Inspect: A technology probe for machine learning audits on enwiki

  arXiv (Cornell University) · 2024-06-12

  preprintOpen accessSenior author

  Auditing the machine learning (ML) models used on Wikipedia is important for ensuring that vandalism-detection processes remain fair and effective. However, conducting audits is challenging because stakeholders have diverse priorities and assembling evidence for a model's [in]efficacy is technically complex. We designed an interface to enable editors to learn about and audit the performance of the ORES edit quality model. ORES-Inspect is an open-source web tool and a provocative technology probe for researching how editors think about auditing the many ML models used on Wikipedia. We describe the design of ORES-Inspect and our plans for further research with this system.

  PublisherOA PDFDOI

Recent grants

• SoCS: Collaborative Research: Novel Algorithms and Interaction Mechanisms to Enhance Social Production

  NSF · $543k · 2012–2016

• HCC: Small: Tools and Mechanisms to Support Civic GeoCampaigns

  NSF · $515k · 2012–2017

• Collaborative Research: Supporting Newcomer Socialization in Online Production Communities

  NSF · $301k · 2011–2016

• CHS:Small:Collaborative Research: Structured Data Peer Production: Addressing Challenges and Leveraging Opportunities

  NSF · $250k · 2018–2022

• Collaborative Research: Mark This! - Operationalizing the Notion of "Place" For Interactive Community Systems

  NSF · $180k · 2003–2007

Frequent coauthors

• Brian Amento

  AT&T (United States)

  23 shared

• Will Hill

  21 shared

• Joseph A. Konstan

  University of Minnesota

  19 shared

• Brent Hecht

  Northwestern University

  19 shared

• Pamela Ludford

  University of Minnesota

  18 shared

• Dan Frankowski

  16 shared

• Jacob Thebault-Spieker

  University of Wisconsin–Madison

  14 shared

• John Riedl

  United Monolithic Semiconductor (France)

  14 shared

Labs

• GroupLensPI

  GroupLens is a research group at the University of Minnesota that focuses on recommender systems and social computing.

Education

• Ph.D., Computer Science

  University of Minnesota

  1990

• M.S., Computer Science

  University of Minnesota

  1986

• B.S., Computer Science

  University of California, Santa Barbara

  1983

Awards & honors

• SIGCHI Lifetime Service Award