Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

Successful online communities accumulate large amounts of long-term content. However there has been little quantitative, theoretically-motivated exploration of how communities organize such content, nor which community members take responsibility for active organization. We examine one aspect of long-term content organization through link behavior, also exploring role differences between enterprise community leaders and members in the context of life-cycle community models. We first classify how content is linked within posts, identifying usage patterns that organize information within and outside communities. We next present an exploratory quantitative analysis of 2,010 communities including 428,476 posts and 1,246,570 links. We show paradoxically that although mature communities accumulate substantial content, organizing that content using links decreases over time. Further analyses suggest that this arises from a recency bias, with communities being focused on current content. Our results also challenge descriptive lifecycle community models, which propose that regular community members adopt greater responsibility over time. We explore explanations for our findings and implications including new tools that encourage responsibility for active organization, as well as methods for members to revisit critical content.

Research on small team collaboration repeatedly shows that 'distance matters'. More recent work has refined this concept of distance to develop geographic dispersion measures to explain the negative effects that team configuration has on productivity and interaction. Dispersion measures explain why teams distributed across multiple time zones, or across multiple sites, have more coordination difficulties than collocated teams with a single remote member. Although larger online communities are increasingly used in enterprises, few studies have examined the effects of dispersion on community behavior. We studied 1206 online enterprise communities (OECs), each using a set of collaborative tools. We present new data showing counter-intuitively that OEC dispersion does not affect content generation or contribution inequality, even when restricting community size to those that most resemble small teams (with 3-12 members). We found that previously documented negative effects of geographic dispersion seem to be reduced in enterprise communities regardless of size. Our results provide additional support to prior case studies suggesting that online communities can mitigate geographic dispersion by providing resources that support coordination and resource sharing.

Recent research has developed analytics that threaten online self-presentation and privacy by automatically generating profiles of individuals' most personal traits-their personality, values, motivations, and so on. But we know little about people's reactions to personal traits profiles of themselves, or what influences their decisions to share such profiles. We present an early qualitative study of people's reactions to a working hyper-personal analytics system. The system lets them see their personality and values profile derived from their own social media text. Our results reveal a paradox. Participants found their personal traits profiles creepily accurate and did not like sharing them in many situations. However, they felt pressured by the social risks of not sharing and showed signs of learned helplessness, leading them to share despite their misgivings. Further, they felt unqualified to significantly modify their profile contents due to a surprising trust in the "expert" algorithm. We explore design implications for hyper-personal analytics systems that consider the needs and preferences of the people being profiled, suggesting ways to enhance the control they feel and the benefits they reap.