Better Work and Career

会議の名前
CHI 2025
“Flexible Platforms? An Ethnographic Study of Flexible Scheduling in Platform-Mediated Delivery
要旨

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.

著者
Kalle Kusk
Aarhus University, Aarhus, Denmark
DOI

10.1145/3706598.3713182

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713182

動画
Exploring Data-Driven Advocacy in Home Health Care Work
要旨

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.

受賞
Best Paper
著者
Joy Ming
Cornell, Ithaca, New York, United States
Hawi H. Tolera
Cornell University, Ithaca, New York, United States
Jiamin Tu
Cornell University, Ithaca, New York, United States
Ella Yitzhaki
Cornell University, Ithaca, New York, United States
Chit Sum Eunice Ngai
Cornell University, Ithaca, New York, United States
Madeline Sterling
Weill Cornell Medicine, New York, New York, United States
Ariel C. Avgar
Cornell University, Ithaca, New York, United States
Aditya Vashistha
Cornell University, Ithaca, New York, United States
Nicola Dell
Cornell Tech, New York, New York, United States
DOI

10.1145/3706598.3713086

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713086

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"Who is running it?" Towards Equitable AI Deployment in Home Care Work
要旨

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.

著者
Ian René. Solano-Kamaiko
Cornell Tech, New York, New York, United States
Melissa Tan
Cornell Tech, New York, New York, United States
Joy Ming
Cornell, Ithaca, New York, United States
Ariel C. Avgar
Cornell University, Ithaca, New York, United States
Aditya Vashistha
Cornell University, Ithaca, New York, United States
Madeline Sterling
Weill Cornell Medicine, New York, New York, United States
Nicola Dell
Cornell Tech, New York, New York, United States
DOI

10.1145/3706598.3713850

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713850

動画
Walk in Their Shoes to Navigate Your Own Path: Learning About Procrastination Through A Serious Game
要旨

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.

著者
Runhua ZHANG
Tongji University, Shanghai, China
Jiaqi Gan
Independent Researcher, San Carlos, California, United States
Shangyuan GAO
Tongji University, Shanghai, Shanghai, China
Siyi Chen
Tongji University, Shanghai, China
Xinyu WU
Tongji University, Shanghai, Shanghai, China
Dong Chen
Tongji University, Shanghai, China
Yulin Tian
Tongji University, Shanghai, China
Qi Wang
Tongji University, Shanghai, China
Pengcheng An
Southern University of Science and Technology, Shenzhen, China
DOI

10.1145/3706598.3715271

論文URL

https://dl.acm.org/doi/10.1145/3706598.3715271

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration
要旨

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.

受賞
Best Paper
著者
Hayeon Jeon
Seoul National University, Seoul, Korea, Republic of
Suhwoo Yoon
Seoul National University, Seoul, Korea, Republic of
Keyeun Lee
University, Seoul, Korea, Republic of
Seo Hyeong Kim
Seoul National University, Seoul, Korea, Republic of
Esther Hehsun Kim
Seoul National University, Seoul, Korea, Republic of
Seonghye Cho
Seoul National University, Seoul, Korea, Republic of
Yena Ko
Seoul National University, Seoul, Korea, Republic of
Soeun Yang
Seoul National University, Seoul, Korea, Republic of
Laura Dabbish
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
John Zimmerman
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Eun-mee Kim
Seoul National University, Seoul, Korea, Republic of
Hajin Lim
Seoul National University , Seoul, Korea, Republic of
DOI

10.1145/3706598.3714206

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714206

動画
Gig2Gether: Datasharing to Empower, Unify and Demistify Gig Work
要旨

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.

受賞
Honorable Mention
著者
Jane Hsieh
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Angie Zhang
University of Texas at Austin, Austin, Texas, United States
Sajel Surati
Bowdoin College, Brunswick, Maine, United States
Sijia Xie
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Yeshua Ayala
Washington University in St. Louis, St. Louis, Missouri, United States
Nithila Sathiya
University of Texas at Austin, Austin, Texas, United States
Tzu-Sheng Kuo
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Min Kyung Lee
University of Texas at Austin, Austin, Texas, United States
Haiyi Zhu
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI

10.1145/3706598.3714398

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714398

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Nurturing Capabilities: Unpacking the Gap in Human-Centered Evaluations of AI-Based Systems
要旨

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.

著者
Aman Khullar
Georgia Institute of Technology, Atlanta, Georgia, United States
Nikhil Nalin
Noora Health, Bangalore, India
Abhishek Prasad
Noora Health, Bangalore, India
Ann John Mampilli
Noora Health, Bangalore, India
Neha Kumar
Georgia Tech, Atlanta, Georgia, United States
DOI

10.1145/3706598.3713278

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713278

動画