Worker-Centered AI for Crowd Work

Task recommendation algorithms in crowd work platforms can erode worker autonomy through strategies such as amplifying the visibility of undesirable or low-paying jobs to meet client demand. We explore whether transparency can shift these systems toward worker-centered values. We built a simulated crowd work platform comparing an opaque Baseline interface against a Transparent variant with explained recommendations and navigation cues. In a within-subjects lab study capturing an initial thirty-minute interaction (n=40), participants rated the transparent system substantially higher on fairness, trust, empowerment, and usability. Behaviorally, transparency supported more strategic task evaluation and increased recommendation alignment. Our results suggest that transparency, even in the form of lightweight disclosure, can improve trust and experience without altering the underlying algorithm. Therefore, we discuss design implications for worker-centered systems, including explanation scope and deployment trade-offs, while acknowledging that durable empowerment requires pairing transparency with meaningful agency and field validation under real economic constraints.

Crowdsourcing platforms employ millions of workers worldwide to complete microtasks that fuel machine-learning pipelines, moderate user-generated content, and accelerate scientific discovery. The rapid ascent of large language models and other foundation models has shifted research attention from automation instead of humans to augmentation alongside humans. This chapter addresses a central challenge in this new landscape: understanding how, how much, and for whom AI-driven tools truly empower crowdworkers. We synthesize insights from a diverse body of empirical studies, spanning randomized lab experiments, field A/B tests, trace-log meta-analyses, and qualitative user studies, to evaluate the impact of AI on crowdwork. To structure this analysis, we introduce a multidimensional empowerment framework grounded in psychological and economic theory, focusing on five key dimensions: productivity and earnings, quality and reliability, learning and skill development, agency and autonomy, and well-being and inclusion. Our review of the evidence reveals a dual-edged reality: while AI assistance offers significant productivity gains, error reductions, and improvements in worker self-efficacy, it also introduces substantial risks, including automation bias, cultural exclusion, wage depression, and unequal access to tools. By distilling these findings, we formulate eight concrete design principles for creating more equitable and effective human-AI augmentation systems. The chapter concludes by outlining a forward-looking research agenda that treats crowdworkers not as replaceable cogs but as essential partners in designing the future of socio-technical systems.

Crowdsourcing markets are expanding worldwide, but often feature standardized interfaces that ignore the cultural diversity of their workers, negatively impacting their well-being and productivity. To transform these workplace dynamics, this paper proposes creating culturally-aware workplace tools, specifically designed to adapt to the cultural dimensions of monochronic and polychronic work styles. We illustrate this approach with "CultureFit," a tool that we engineered based on extensive research in Chronemics and culture theories. To study and evaluate our tool in the real world, we conducted a field experiment with 55 workers from 24 different countries. Our field experiment revealed that CultureFit significantly improved the earnings of workers from cultural backgrounds often overlooked in design. Our study is among the pioneering efforts to examine culturally aware digital labor interventions. It also provides access to a dataset with over two million data points on culture and digital work, which can be leveraged for future research in this emerging field. The paper concludes by discussing the importance and future possibilities of incorporating cultural insights into the design of tools for digital labor.

Crowdsourcing platforms gather people from different backgrounds to work on completing microtasks. The workers on those platforms are presented with tasks defined by requesters from all over the world. The tasks are defined in multiple languages, with English being the predominant language. It is unclear how language barriers can affect workers whose primary language is not English while completing tasks. In this paper, we study their practices and the impact of AI-powered tools on the completion of microtasks. We conduct a pre-test test field experiment in which workers share their current experience with the platform and receive basic training in the use of AI-powered tools. After a week, workers provide feedback on their practices and experiences. We find that workers who complete tasks in their primary language report less confidence in completing English tasks, and in general, workers perceive the use of the tools as a language-learning mechanism. We aim that the understanding of language barriers in crowd markets can promote more inclusive work conditions.

The artificial intelligence industry has been essential in creating new jobs for the deployment of real-world solutions. As a result, the implementation of these new jobs involves the execution of multiple human intelligence micro-tasks, such as data labeling tasks for training machine learning models. The workers who perform those tasks, also known as crowd workers, usually are independent workers within crowdsourcing platforms. These platforms are subject to the free market, where the forces of supply and demand produce various power dynamics among stakeholders. As a result, disassociation between stakeholders often generates unbalanced power dynamics where workers are paid below minimum wage and are intimidated to keep their reputation or face termination. Within this chapter, we introduce computational techniques to audit the workplace conditions of crowd workers and design tools to address these power imbalances, as a positive and more efficient alternative for the labor conditions of crowd workers. This chapter develops these objectives through the design and evaluation of three tools in digital labor platforms: “Invisible Labor Tracker,” “Reputation Agent,” and “CultureFit,” which we describe below. We will demonstrate the sustainability of systems that point to a future where AI can be used to audit and address power imbalances in the workplace.