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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., Comment: To appear in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '25)

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., Comment: Wiki Workshop 2024

Social technologies are the systems, interfaces, features, infrastructures, and architectures that allow people to interact with each other online. These technologies dramatically shape the fabric of our everyday lives, from the information we consume to the people we interact with to the foundations of our culture and politics. While the benefits of social technologies are well documented, the harms, too, have cast a long shadow. To address widespread problems like harassment, disinformation, information access, and mental health concerns, we need to rethink the foundations of how social technologies are designed, sustained, and governed. This report is based on discussions at the Computing Community Consortium Workshop, The Future of Research on Social Technologies, that was held November 2-3, 2023 in Washington, DC. The visioning workshop came together to focus on two questions. What should we know about social technologies, and what is needed to get there? The workshop brought together over 50 information and computer scientists, social scientists, communication and journalism scholars, and policy experts. We used a discussion format, with one day of guiding topics and a second day using an unconference model where participants created discussion topics. The interdisciplinary group of attendees discussed gaps in existing scholarship and the methods, resources, access, and collective effort needed to address those gaps. We also discussed approaches for translating scholarship for various audiences including citizens, funders, educators, industry professionals, and policymakers. This report presents a synthesis of major themes during our discussions. The themes presented are not a summary of what we know already, they are an exploration of what we do not know enough about, and what we should spend more effort and investment on in the coming years.

Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then did a three-month controlled experiment (Study 2). Our results show that presenting users with articles from underrepresented topics increased the proportion of work done on those articles without significantly reducing overall recommendation uptake. We discuss the implications of our results, including how ignoring the article discovery process can artificially narrow recommendations on peer production platforms., Comment: To appear at the 18th International AAAI Conference on Web and Social Media (ICWSM 2024)

Workplace bias creates negative psychological outcomes for employees, permeating the larger organization. Workplace meetings are frequent, making them a key context where bias may occur. Video conferencing (VC) is an increasingly common medium for workplace meetings; we therefore investigated how VC tools contribute to increasing or reducing bias in meetings. Through a semi-structured interview study with 22 professionals, we found that VC features push meeting leaders to exercise control over various meeting parameters, giving leaders an outsized role in affecting bias. We demonstrate this with respect to four core VC features -- user tiles, raise hand, text-based chat, and meeting recording -- and recommend employing at least one of two mechanisms for mitigating bias in VC meetings -- 1) transferring control from meeting leaders to technical systems or other attendees and 2) helping meeting leaders better exercise the control they do wield., Comment: To appear at the 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2023)

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 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., Comment: CSCW 2025, 24 article pages, 34 pages of references & appendices, 17 figures

Wikipedia -- like most peer production communities -- suffers from a basic problem: the amount of work that needs to be done (articles to be created and improved) exceeds the available resources (editor effort). Recommender systems have been deployed to address this problem, but they have tended to recommend work tasks that match individuals' personal interests, ignoring more global community values. In English Wikipedia, discussion about Vital articles constitutes a proxy for community values about the types of articles that are most important, and should therefore be prioritized for improvement. We first analyzed these discussions, finding that an article's priority is considered a function of 1) its inherent importance and 2) its effects on Wikipedia's global composition. One important example of the second consideration is balance, including along the dimensions of gender and geography. We then conducted a quantitative analysis evaluating how four different article prioritization methods -- two from prior research -- would affect Wikipedia's overall balance on these two dimensions; we found significant differences among the methods. We discuss the implications of our results, including particularly how they can guide the design of recommender systems that take into account community values, not just individuals' interests., Comment: To appear at the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2022)

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems., Comment: Pre-print of paper conditionally accepted to CSCW 2020. 31 main-article pages; 46 pages with appendix

On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach to understanding a community-created and -maintained machine learning-based algorithm called the Objective Revision Evaluation System (ORES)---a quality prediction system used in numerous Wikipedia applications and contexts. Five major values converged across stakeholder groups that ORES (and its dependent applications) should: (1) reduce the effort of community maintenance, (2) maintain human judgement as the final authority, (3) support differing peoples' differing workflows, (4) encourage positive engagement with diverse editor groups, and (5) establish trustworthiness of people and algorithms within the community. We reveal tensions between these values and discuss implications for future research to improve algorithms like ORES., Comment: 10 pages, 1 table, accepted paper to CHI 2020 conference

Artificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stakes contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs. We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating., Comment: Paper appeared at Proceedings of The 2020 ACM conference on Designing Interactive Systems (DIS'20)