This paper explores flexibility in platform-mediated work through a multi-sited ethnographic study of delivery workers' "flexible scheduling" in three European countries: Denmark, Finland, and Malta. While workers generally value the ability to schedule flexibly, this flexibility is constrained by structural factors such as piece-rate remuneration, demand fluctuations, surge pricing, and income dependency. The constraints result in markedly different experiences across the different instantiations of the same, standardised delivery platform: workers in Denmark benefit from the system, in Finland workers face seasonal precarity, and in Malta workers endure exploitative cycles of long hours and low pay. The findings demonstrate how the same platform's standardised design can produce divergent outcomes in local contexts. The paper highlights the need for platform designers and regulators to balance the benefits of flexible scheduling with its trade-offs, ensuring that flexibility supports worker well-being as the flexible platforms manifest locally.
This paper explores opportunities and challenges for data-driven advocacy to support home care workers, an often overlooked group of low-wage, frontline health workers. First, we investigate what data to collect and how to collect it in ways that preserve privacy and avoid burdening workers. Second, we examine how workers and advocates could use collected data to strengthen individual and collective advocacy efforts. Our qualitative study with 11 workers and 15 advocates highlights tensions between workers’ desires for individual and immediate benefits and advocates’ preferences to prioritize more collective and long-term benefits. We also uncover discrepancies between participants’ expectations for how data might transform advocacy and their on-the-ground experiences collecting and using real data. Finally, we discuss future directions for data-driven worker advocacy, including combining different kinds of data to ameliorate challenges, leveraging advocates as data stewards, and accounting for workers’ and organizations’ heterogeneous goals.
We present a qualitative study that investigates the implications of current and near-future AI deployment for home care workers (HCWs), an overlooked group of frontline healthcare workers. Through interviews with 22 HCWs, care agency staff, and worker advocates, we find that HCWs do not understand how AI works, how their data can be used, or why AI systems might retain their information. HCWs are unaware that AI is already being utilized in their work, primarily via algorithmic shift-matching systems adopted by agencies. Participants detail the risks AI poses in sensitive care settings for HCWs, patients, and agencies, including threats to workers' autonomy and livelihoods, and express concerns that workers will be held accountable for AI mistakes, with the burden of proving AI's decisions incorrect falling on them. Considering these risks, participants advocate for new regulations and democratic governance structures that protect workers and control AI deployment in home care work.
Procrastination, the voluntary delay of tasks despite potential negative consequences, has prompted numerous time and task management interventions in the HCI community. While these interventions have shown promise in addressing specific behaviors, psychological theories suggest that learning about procrastination itself may help individuals develop their own coping strategies and build mental resilience. However, little research has explored how to support this learning process through HCI approaches. We present ProcrastiMate, a text adventure game where players learn about procrastination's causes and experiment with coping strategies by guiding in-game characters in managing relatable scenarios. Our field study with 27 participants revealed that ProcrastiMate facilitated learning and self-reflection while maintaining psychological distance, motivating players to integrate newly acquired knowledge in daily life. This paper contributes empirical insights on leveraging serious games to facilitate learning about procrastination and offers design implications for addressing psychological challenges through HCI approaches.
Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants’ future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants' engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults' career exploration.
The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future uses cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.
Human-Computer Interaction (HCI) scholarship has studied how Artificial Intelligence (AI) can be leveraged to support care work(ers) by recognizing, reducing, and redistributing workload. Assessment of AI's impact on workers requires scrutiny and is a growing area of inquiry within human-centered evaluations of AI. We add to these conversations by unpacking the sociotechnical gap between the broader aspirations of workers from an AI-based system and the narrower existing definitions of success. We conducted a mixed-methods study and drew on Amartya Sen's Capability Approach to analyze the gap. We shed light on the social factors---on top of performance on evaluation metrics---that guided the AI model choice and determined whose wellbeing must be evaluated while conducting such evaluations. We argue for assessing broader achievements enabled through AI's use when conducting human-centered evaluations of AI. We discuss and recommend the dimensions to consider while conducting such evaluations.