Behind the scenes of maintaining the safety of technology products from harmful and illegal digital content lies unrecognized human labor. The recent rise in the use of generative AI technologies and the accelerating demands to meet responsible AI (RAI) aims necessitates an increased focus on the labor behind such efforts in the age of AI. This study investigates the nature and challenges of content work that supports RAI efforts, or "RAI content work," that spans content moderation, data labeling, and red teaming -- through the lived experiences of content workers. We conduct a formative survey and semi-structured interview studies to develop a conceptualization of RAI content work and a subsequent framework of recommendations for providing holistic support for content workers. We validate our recommendations through a series of workshops with content workers and derive considerations for and examples of implementing such recommendations. We discuss how our framework may guide future innovation to support the well-being and professional development of the RAI content workforce.

Sharing personal pronouns (such as they/them, she/her, or he/him) in the workplace helps colleagues respectfully address each other. Many companies currently seek to design and implement new software tools to facilitate pronoun-sharing among employees. This paper analyzes the social processes of communication about pronouns in the workplace and identifies best practices for creating and using workplace pronoun-sharing software. We conducted 78 semi-structured qualitative interviews with various stakeholders involved in the launch of pronoun-sharing tools in workplace collaboration software, including transgender and queer people, HR and IT professionals, and LGBTQ advocacy organizations. We used an anthropological approach to qualitatively analyze interview materials and notes from interactions with research participants. We find that sharing personal pronouns is an ongoing communication process rather than a single act of information provision. Pronoun sharing tools encapsulate the tension between dynamic social processes of self-expression and technical systems of classification and information retrieval. People communicate their pronouns differently as they navigate identity expression across social contexts. Sharing pronouns is therefore both an individual expression of self-presentation and a complex act of social communication. Developers must create new methods for building pronoun-sharing tools that equip people to control ongoing social processes of self-expression instead of using an information retrieval approach that treats pronouns merely as stable, unchanging data.

With a seemingly endless stream of tasks, on-demand labor markets appear to offer workers flexibility in when and how much they work. This research argues that platforms afford workers far less flexibility than widely believed. A large part of the "inflexibility" comes from tight deadlines imposed on tasks, leaving workers little control over their work schedules. We experimentally examined the impact of offering workers control of their time in on-demand crowdwork. We found that granting higher "in-task flexibility" dramatically affected the temporal dynamics of worker behavior and produced a larger amount of work with similar quality. In a second experiment, we measured the compensating differential and found that workers would give up significant compensation to control their time, indicating workers attach substantial value to in-task flexibility. Our results suggest that designing tasks which give workers direct control of their time within tasks benefits both buyers and sellers of on-demand crowdwork.

Numerous crowdsourcing platforms are now available to support research as well as commercial goals. However, crowdsourcing is not yet widely adopted by researchers for generating, processing or analyzing research data. This study develops a deeper understanding of the circumstances under which crowdsourcing is a useful, feasible or desirable tool for research, as well as the factors that may influence researchers' decisions around adopting crowdsourcing technology. We conducted semi-structured interviews with 18 researchers in diverse disciplines, spanning the humanities and sciences, to illuminate how research norms and practitioners' dispositions were related to uncertainties around research processes, data, knowledge, delegation and quality. The paper concludes with a discussion of the design implications for future crowdsourcing systems to support research.

The main goal of this paper is to show that crowdworkers collaborate to fulfill technical and social needs left by the platform they work on. That is, crowdworkers are not the independent, autonomous workers they are often assumed to be, but instead work within a social network of other crowdworkers. Crowdworkers collaborate with members of their networks to 1) manage the administrative overhead associated with crowdwork, 2) find lucrative tasks and reputable employers and 3) recreate the social connections and support often associated with brick and mortar-work environments. Our evidence combines ethnography, interviews, survey data and larger scale data analysis from four crowdsourcing platforms, emphasizing the qualitative data from the Amazon Mechanical Turk (MTurk) platform and Microsoft's proprietary crowdsourcing platform, the Universal Human Relevance System (UHRS). This paper draws from an ongoing, longitudinal study of Crowdwork that uses a mixed methods approach to understand the cultural meaning, political implications, and ethical demands of crowdsourcing.

Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the "crowd" has been viewed as a group of independent workers dispersed all over the world. Recent studies based on in-person interviews have opened up the black box and shown that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. Put another way, prior work has shown the existence of edges between workers. We build on and extend this discovery by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We execute a task in which over 10,000 workers from across the globe self-report their communication links to other workers, thereby mapping the communication network among workers. Our results suggest that while a large percentage of workers indeed appear to be independent, there is a rich network topology over the rest of the population. That is, there is a substantial communication network within the crowd. We further examine how online forum usage relates to network topology, how workers communicate with each other via this network, how workers' experience levels relate to their network positions, and how U.S. workers differ from international workers in their network characteristics. We conclude by discussing the implications of our findings for requesters, workers, and platform providers like Amazon.

We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.