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Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges. 

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. 

A major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use. This paper evaluates quality estimation feedback in vivo with a human study in realistic high-stakes medical settings. Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses. 

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. 

In the United States, public defenders (lawyers assigned to people accused of crimes who cannot 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 increases in the volume and complexity of data in modern criminal cases. We explore the technology needs of public defenders through a series of semi-structured interviews with public defenders and those 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) more control over the type of information used in criminal cases than defense attorneys. We find that public defenders may be able to create fairer situations for their clients with better tools for data interpretation and access. Therefore, we call on technologists to attend to the needs of public defenders and the people they represent when designing systems that collect data about people. Our findings illuminate constraints that technologists and privacy advocates should consider as they pursue solutions. In particular, our work complicates notions of individual privacy as the only value in protecting users' rights, and demonstrates the importance of data interpretation alongside data visibility. As data sources become more complex, control over the data cannot be separated from access to the experts and technology to make sense of that data. 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. 

HCI has long considered sites of workplace collaboration. From airline cockpits to distributed groupware systems, scholars emphasize the importance of supporting a multitude of tasks and creating technologies that integrate into collaborative work settings. More recent scholarship highlights a growing need to consider the concerns of workers within and beyond established workplace settings or roles of employment, from steelworkers whose jobs have been eliminated with post-industrial shifts in the economy to contractors performing the content moderation that shapes our social media experiences. This one-day workshop seeks to bring together a growing community of HCI scholars concerned with the labor upon which the future of work we envision relies. We will discuss existing methods for studying work that we find both productive and problematic, with the aim of understanding how we might better bridge current gaps in research, policy, and practice. Such conversations will focus on the challenges associated with taking a worker-centered approach and outline concrete methods and strategies for conducting research on labor in changing industrial, political, and environmental contexts. 

Many decisions about social, economic, and personal life are heavily data-driven. At the same time, data has become increasingly quantified, and available to people and institutions in positions of power, often with little introspection or reflection on its positive uses or harmful misuses. This panel will inspect CSCW’s role in identifying constructive and appropriate uses of data and its responsibility for protecting against harms and inequalities perpetuated by misuse. The panel will present a series of debates about quantification of data, data surveillance, organizational data use, and policy making. An overarching theme throughout the set of debates is interrogating CSCW’s role in extending critical scholarship on power and justice towards academic, policy, and industry impact. 

As people increasingly work different jobs, the responsibility of building long-term career satisfaction and stability increasingly falls more on the workers rather than individual employers. With technologies playing a central role in how people choose and access employment opportunities, it is necessary to understand how online technologies are shaping career development. We bring together leading human-computer interaction researchers, industry members, and community organizers, who have worked with systems and people across the socio-economic spectrum during the career development process. We will discuss research on the role of online technologies in career development, including but not limited to topics in crowd work, social media sites, and freelance work sites.