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This paper studies the design of an AI tool that supports gig knowledge workers, rather than displacing them, focusing on text-based generative AI technologies. Through a formative study involving interviews and design activities, gig workers shared their views on text-based generative AI and envisioned applications where AI acts as managers, secretaries, and communication aids. Leveraging these insights, we created a generative-AI enhanced tool, Office-Mind AI, to aid gig workers. Our research advances the conversation around algorithmic labor by designing a worker-focused intelligent tool. This tool harness collective intelligence among workers and AI, fostering productive human-AI partnerships. We conclude by discussing the future prospects of collective intelligence tools designed for worker-AI collaborations.
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Unveiling AI-Driven Collective Action for a Worker-Centric Future
Collective action by gig knowledge workers is a potent method for enhancing labor conditions on platforms like Upwork, Amazon Mechanical Turk, and Toloka. However, this type of collective action is still rare today. Existing systems for supporting collective action are inadequate for workers to identify and understand their different workplace problems, plan effective solutions, and put the solutions into action. This talk will discuss how with my research lab we are creating worker-centric AI enhanced technologies that enable collective action among gig knowledge workers. Building solid AI enhanced technologies to enable gig worker collective action will pave the way for a fair and ethical gig economy—one with fair wages, humane working conditions, and increased job security. I will discuss how my proposed approach involves first integrating "sousveillance," a concept by Foucault, into the technologies. Sousveillance involves individuals or groups using surveillance tools to monitor and record those in positions of power. In this case, the technologies enable gig workers to monitor their workplace and their algorithmic bosses, giving them access to their own workplace data for the first time. This facilitates the first stage of collective action: problem identification. I will then discuss how we combine this data with Large-Language-Models (LLMs) and social theories to create intelligent assistants that guide workers to complete collective action via sensemaking and solution implementation. The talk will present a set of case studies to showcase this vision of designing data driven AI technologies to power gig worker collective action. In particular, I will present the systems: 1) GigSousveillance which allows workers to monitor and collect their own job-related data, facilitating quantification of workplace problems; 2) GigSense equips workers with an AI assistant that facilitates sensemaking of their work problems, helping workers to strategically devise solutions to their challenges; 3) GigAction is an AI assistant that guides workers to implement their proposed solutions. I will discuss how we are designing and implementing these systems by adopting a participatory design approach with workers, while also conducting experiments and longitudinal deployments in the real world. I conclude by presenting a research agenda for transforming and rethinking the role of A.I. in our workplaces; and researching effective socio-technical solutions in favor of a worker-centric future and countering technoauthoritarianism
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- Award ID(s):
- 2339443
- PAR ID:
- 10590843
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400703713
- Page Range / eLocation ID:
- 6 to 7
- Subject(s) / Keyword(s):
- gig workers colllaboration wsdm
- Format(s):
- Medium: X
- Location:
- Merida Mexico
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3616855.3637633
