This study examines the expectations that workers have regarding enterprise social media (ESM). Using interviews with 58 employees at an organization implementing an

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.

Social media continues to grow as a focus of social, organizational, and scholarly interest, yet there is little agreement as to what constitutes social media and how it can be effectively analyzed. We review various definitions of social media and note that much of the confusion regarding social media comes from conflation between social media types, platforms, and activities. To facilitate investigations of social media, we debunk common social media myths and review the relationship between social media and several prominent sociological concerns. We conclude by reflecting on directions for future research on social media.

Diane E. Bailey is an associate professor at the School of Information at the University of Texas at Austin, where she studies technology and work in information and technical occupations. Her research interests include work and artificial intelligence, computational technologies in engineering design, remote occupational socialization, and Information and Communications Technologies for Development (ICT4D).

Although voice assistants are increasingly being adopted by older adults, we lack empirical research on how they interact with these devices for health information seeking. Also, prior work shows how voice assistant responses can provide misleading or inaccurate information and be harmful particularly in health contexts. Because of increased health needs while aging, this paper studies older adult’s (ages 65+) health-related voice assistant interactions. Motivated by a lack of empirical evidence for how older adults approach information seeking with emerging technologies, we first conducted a survey of n = 201 older adults to understand how they engage voice assistants compared to a range of offline and digital sources for health information seeking. Findings show how voice assistants were used for confirmatory health queries, with users showing signs of distrust. As much prior work focuses on perceptions of voice assistant use, we conducted scenario-based interviews with n = 35 older adults to study health-related voice assistant behavior. In interviews, participants engaged with different health topics (flu, migraine, high blood pressure) and scenario types (symptom-driven, behavior-driven) using a voice assistant. Findings show how conversational and human-like expectations with voice assistants lead to information breakdowns between the older adult and voice assistant. This paper contributes a nuanced query-level analysis of older adults’ voice-based health information seeking behaviors. Further, data provide evidence for how query reformulation happens with complex topics in voice-based information seeking. We use our findings to discuss how voice interfaces can better support older adults’ health information seeking behaviors and expectations.