SnuggleSense: Empowering Online Harm Survivors Through a Structured Sensemaking Process Online interpersonal harm, such as cyberbullying and sexual harassment, remains a pervasive issue on social media platforms. Traditional approaches, primarily content moderation, often overlook survivors' needs and agency. We introduce SnuggleSense, a system that empowers survivors through structured sensemaking.
Inspired by restorative justice practices, SnuggleSense guides survivors through reflective questions, offers personalized recommendations from similar survivors, and visualizes plans using interactive sticky notes. A controlled experiment demonstrates that SnuggleSense significantly enhances sensemaking compared to an unstructured process of making sense of the harm. We argue that SnuggleSense fosters community awareness, cultivates a supportive survivor network, and promotes a restorative justice-oriented approach toward restoration and healing. We also discuss design insights, such as tailoring informational support and providing guidance while preserving survivors' agency.
Trauma & Abuse
CSCW 2025 Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use AI systems and tools today can generate human-like expressions on behalf of people. It raises the crucial question about how to sustain human agency in AI-mediated communication. We investigated this question in the context of machine translation (MT) assisted conversations. Our participants included 45 dyads. Each dyad consisted of one new immigrant in the United States, who leveraged MT for English information seeking as a non-native speaker, and one local native speaker, who acted as the information provider. Non-native speakers could influence the English production of their message in one of three ways: labeling the quality of MT outputs, regular post-editing without additional hints, or augmented post-editing with LLM-generated hints. Our data revealed a greater exercise of non-native speakers’ agency under the two post-editing conditions. This benefit, however, came at a significant cost to the dyadic-level communication performance. We derived insights for MT and other generative AI design from our findings.
YX
Yimin Xiao et al. University of Maryland, College of Information
Multilingual & Cross-Cultural Voice Interaction Generative AI (Text, Image, Music, Video) Human-LLM Collaboration
CHI 2025 emoji_events Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple platforms to make informed decisions. Drawing on their vision of an ideal knowledge synthesis tool, we developed Yodeai, an AI-enabled system, to explore both the opportunities and limitations of AI in knowledge work. Through a user study with 16 product managers, we identified three key requirements for Generative AI in knowledge work: adaptable user control, transparent collaboration mechanisms, and the ability to integrate background knowledge with external information. However, we also found significant limitations, including overreliance on AI, user isolation, and contextual factors outside the AI's reach. As AI tools become increasingly prevalent in professional settings, we propose design principles that emphasize adaptability to diverse workflows, accountability in personal and collaborative contexts, and context-aware interoperability to guide the development of human-centered AI systems for product managers and knowledge workers.
BY
Bhada Yun et al. University of California, Berkeley
Generative AI (Text, Image, Music, Video) Human-LLM Collaboration Knowledge Worker Tools & Workflows
CHI 2025 (Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court Accountable use of AI systems in high-stakes settings relies on making systems contestable. In this paper we study efforts to contest AI systems in practice by studying how public defenders scrutinize AI in court. We present findings from interviews with 17 people in the U.S. public defense community to understand their perceptions of and experiences scrutinizing computational forensic software (CFS) --- automated decision systems that the government uses to convict and incarcerate, such as facial recognition, gunshot detection, and probabilistic genotyping tools. We find that our participants faced challenges assessing and contesting CFS reliability due to difficulties (a) navigating how CFS is developed and used, (b) overcoming judges and jurors’ non-critical perceptions of CFS, and (c) gathering CFS expertise. To conclude, we provide recommendations that center the technical, social, and institutional context to better position interventions such as performance evaluations to support contestability in practice.
AJ
Angela Jin et al. University of California, Berkeley
Explainable AI (XAI) AI Ethics, Fairness & Accountability Algorithmic Transparency & Auditability
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Explore Nowopen_in_new Sustained Harm Over Time and Space Limits the External Function of Online Counterpublics for American Muslims Social media platforms are celebrated for their capacity to empower those with marginalized and disenfranchised identities and support them to create counterpublics. We focus on one such group, Muslim Americans, and ask how visible Muslim Americans, such as journalists, activists, and aspiring politicians, use social media to craft counter-narratives, reclaim control of their stories, and mitigate the harm directed at them. Through a series of 19 interviews, we found that visible Muslim Americans’ ability to craft and sustain counter narratives is largely hampered by sustained online harm. We found that these public figures were harmed repeatedly over long periods of time and through the weaponization of platform affordances such as replying, tagging, and hashtag takeovers, as well as their gender and identity. Our findings shed light on the serious limitations of social media to provide a safe platform for counterpublics to engage externally with wider publics. Finally, we discuss the limitations of content moderation as the dominant framework for addressing harm online and suggest alternative paths forward based on restorative and transformative justice.
Harm and Vulnerability
CSCW 2023 Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making.
SR
Samantha Robertson et al. University of California, Berkeley
AI-Assisted Decision-Making & Automation Algorithmic Fairness & Bias Participatory Design
CHI 2023 Not Another School Resource Map: Meeting Underserved Families' Information Needs Requires Trusting Relationships and Personalized Care Public school districts across the United States have implemented school choice systems that have the potential to improve underserved students' access to educational opportunities. However, research has shown that learning about and applying for schools can be extremely time-consuming and expensive, making it difficult for these systems to create more equitable access to resources in practice. A common factor surfaced in prior work is unequal access to information about the schools and enrollment process. In response, governments and non-profits have invested in providing more information about schools to parents, for instance, through detailed online dashboards. However, we know little about what information is actually useful for historically marginalized and underserved families. We conducted interviews with 10 low-income families and families of color to learn about the challenges they faced navigating an online school choice and enrollment system. We complement this data with four interviews with people who have supported families through the enrollment process in a wide range of roles, from school principal to non-profit staff (``parent advocates''). Our findings highlight the value of personalized support and trusting relationships to delivering relevant and helpful information. We contrast this against online information resources and dashboards, which tend to be impersonal, target a broad audience, and make strong assumptions about what parents should look for in a school without sensitivity to families' varying circumstances. We advocate for an assets-based design approach to information support in public school enrollment, which would ask how we can support the local, one-on-one support that community members already provide.
SR
Samantha Robertson et al.
Parenting and Families; Parenting and Families
CSCW 2022 Trial by File Formats: Exploring public defenders' challenges working with novel surveillance data Public defenders, lawyers assigned to people accused of crimes who can not afford a private attorney, serve as an essential bulwark against wrongful arrest and incarceration for low-income and marginalized people. Public defenders have long been overworked and under-resourced, however these issues have been compounded by boosts in the volume and complexity of data in modern criminal defense cases. We explore the technology needs of public defenders through a series of semi-structured interviews with public defenders and the people who work with them. We find that public defenders' ability to reason about novel surveillance data is woefully inadequate not only due to a lack of resources and knowledge, but also due to the structure of the criminal justice system, which gives prosecutors and police (in partnership with private companies) far more control than defense attorneys over the type of information used in criminal cases. We found that public defenders may be able to create fairer situations for their clients with more data access and better tools for data interpretation. Therefore, we call on technologists to keep the potential future needs of public defenders and the people they represent in mind when designing systems that collect data about people. Our findings illuminate constraints that technologists and privacy advocates should consider as they pursue solutions, for instance our work complicates notions of individual privacy as the only value in protecting users' rights and demonstrates the importance's of data interpretation along side data visibility. As data sources become more complex, control over the data cannot be separated from access to experts and technology who can make sense of it. The growing surveillance data ecosystem may systematically oppress not only those who are most closely observed, but groups of people whose communities and advocates have been deprived of the storytelling power over their information.
RW
Rachel B. Warren et al.
Smart Homes, Privacy, and Surveillance; Smart Homes, Privacy, and Surveillance
CSCW 2022 The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating Targeted advertising can harm vulnerable groups when it targets individuals' personal and psychological vulnerabilities. We focus on how targeted weight-loss advertisements harm people with histories of disordered eating. We identify three features of targeted advertising that cause harm: the persistence of personal data that can expose vulnerabilities, over-simplifying algorithmic relevancy models, and design patterns that increase engagement but can also encourage unhealthy behavior. Through a series of semi-structured interviews with individuals with histories of unhealthy body stigma, dieting, and disordered eating, we found that targeted weight-loss ads posed a range of negative emotional and physical outcomes, placing a burden of individual responsibility on the user. At the same time, we observed that targeted individuals demonstrated agency and resistance against distressing ads. Drawing on scholarship in postcolonial environmental studies, we use the concept of slow violence to articulate how targeted advertising inflicts concrete harms on vulnerable populations. CAUTION: This paper includes media that could be triggering, particularly to people with an eating disorder. Please use caution when reading, printing, or disseminating this paper.
Health and Consultation Practices, Addictive Behaviors, and Social Re-entry; Health and Consultation Practices, Addictive Behaviors, and Social Re-entry
CSCW 2022 Bridging Action Frames: Instagram Infographics in U.S. Ethnic Movements Instagram infographics are a digital activism tool that have redefined action frames for technology-facilitated social movements. From the 1960s through the 1980s, United States ethnic movements practiced collective action: ideologically unified, resource-intensive traditional activism. Today, technologically enabled movements have been categorized as practicing connective action: individualized, low-resource online activism. Yet, we argue that Instagram infographics are both connective and collective. This paper juxtaposes the insights of past and present U.S. ethnic movement activists and analyzes Black Lives Matter Instagram data over the course of 7 years (2014-2020). We find that Instagram infographic activism bridges connective and collective action in three ways: (1) Scope for Education: Visually enticing and digestible infographics reduce the friction of information dissemination, facilitating collective movement education while preserving customizability. (2) Reconciliation for Credibility: Activists use connective features to combat infographic misinformation and resolve internal differences, creating a trusted collective movement front. (3) High-Resource Efforts for Transformative Change: Instagram infographic activism has been paired with boots on the ground and action-oriented content, curating a connective-to-collective pipeline that expends movement resources. Our work unveils the vitality of evaluating digital activism action frames at the movement integration level, exemplifies the powerful coexistence of connective and collective action, and offers design implications for activists seeking to leverage this novel tool.
Collective Action; Collective Action
CSCW 2022 Sensemaking, Support, Safety, Retribution, Transformation: Understanding Adolescents' Needs for Addressing Online Harm Online harm is a prevalent issue in adolescents' online lives. Restorative justice teaches us to focus on those who have been harmed, ask what their needs are, and engage in the offending party and community members to collectively address the harm. In this research, we conducted interviews and design activities with harmed adolescents to understand their needs to address online harm. They also identified the key stakeholders relevant to their needs, the desired outcomes, and the preferred timing to achieve them. We identified five central needs of harmed adolescents: sensemaking, emotional support and validation, safety, retribution, and transformation. We find that addressing the needs of those who are harmed online usually requires concerted efforts from multiple stakeholders online and offline. We conclude by discussing how platforms can implement design interventions to meet some of these needs.
SX
Sijia Xiao et al. University of California, Berkeley
Online Harassment & Counter-Tools Cyberbullying & Online Harassment Online Identity & Self-Presentation
CHI 2022 Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.
DX
Doris Xin et al. University of California, Berkeley
AI-Assisted Decision-Making & Automation AutoML Interfaces
CHI 2021 No: Critical Refusal as Feminist Data Practice Critical refusal is a generative concept for challenging harmful data practices, while simultaneously negotiating and developing alternative actions. The panelists will discuss two projects, Data Feminism and The Feminist Data Manifest-No, to illustrate how critical refusal can be used as a tool for generating alternative data practices within CSCW and social computing research. The panel will be of interest to academic and industry attendees who seek to examine concerns of power and privilege, including and reaching beyond gender-based inequalities, within the field of data science. Attendees will also brainstorm refusals and commitments that are meaningful to their research and practice.
No: Critical Refusal as Feminist Data Practice
CSCW 2020 Agent, Gatekeeper, Drug Dealer: How Content Creators Craft Algorithmic Personas Online content creators have to manage their relations with opaque, proprietary algorithms that platforms employ to rank, filter, and recommend content. How do content creators make sense of these algorithms and what does that teach us about the roles that algorithms play in the social world? We take the case of YouTube because of its widespread use and the spaces for collective sense making and mutual aid that content creators (YouTubers) have built within the last decade. We engaged with YouTubers in one-on-one interviews, performed content analysis on YouTube videos that discuss the algorithm, and conducted a wiki survey on YouTuber online groups. This triangulation of methodologies afforded us a rich understanding of content creators' understandings, priorities, and wishes as they relate to the algorithm. We found that YouTubers assign human characteristics to the algorithm to explain its behavior; what we have termed algorithmic personas. We identify three main algorithmic personas on YouTube: Agent, Gatekeeper, and Drug Dealer. We propose algorithmic personas as a conceptual framework that describes the new roles that algorithmic systems take on in the social world. As we face new challenges around the ethics and politics of algorithmic platforms such as YouTube, algorithmic personas describe roles that are familiar and can help develop our understanding of algorithmic power relations and accountability mechanisms.
AI and Fairness
CSCW 2019 Ink: Increasing Worker Agency to Reduce Friction in Hiring Crowd Workers The web affords connections by which end-users can receive paid, expert help—such as programming, design,and writing—to reach their goals. While a number of online marketplaces have emerged to facilitate suchconnections, most end-users do not approach a market to hire an expert when faced with a challenge. To reduce friction in hiring from peer-to-peer expert crowd work markets, we propose Ink, a system that crowd workers can use to showcase their services by embedding tasks inside web tutorials—a common destination for users with information needs. Workers have agency to define and manage tasks, through which users can request their help to review or execute each step of the tutorial, for example, to give feedback on a paper outline, perform a statistical analysis, or host a practice programming interview. In a public deployment,over 25,000 pageviews led 168 tutorial readers to pay crowd workers for their services, most of whom had not previously hired from crowdsourcing marketplaces. A field experiment showed that users were more likely to hire crowd experts when the task was embedded inside the tutorial rather than when they were redirected to the same worker’s Upwork profile to hire them. Qualitative analysis of interviews showed that Ink framed hiring expert crowd workers within users’ well-established information seeking habits and gave workers more control over their work.
Workers and Employees
CSCW 2018