Overview
The widespread adoption of platformized gig work has produced major disruptions to labor patterns and mobility. Underpinning transformative technology are workers, who face challenges to well-being in their labor, including a lack of data transparency, social isolation, and inadequate protections. In this talk, I present Gig2Gether, a prototype data-sharing system evaluated within the context of workers’ everyday labor to explore the possibilities of fostering solidarity, financial reflection, and policy influence across participants of different platforms. I discuss the results of a 7-day field study with 16 active workers spanning three work domains, which reveal existing and envisioned affordances of cross-platform data-sharing for collectivism and policy impact. Workers leveraged shared data to provide mutual support, improve financial planning, and envision new forms of collectivism and policy advocacy around issues like safety and pay. These findings highlight both the potential and the challenges of data-sharing for the gig work ecosystem. We discuss how data-sharing tools can complement existing structures to enhance worker empowerment and drive meaningful policy change.

About the speaker

Jane is a final year software engineering PhD at Carnegie Mellon. Her research seeks to empower and collectivize platform-based laborers by bridging information asymmetries and gaps between worker communities. Specifically, she works with large-scale data and multiple stakeholder groups to (1) draw insights and design interventions to improve the gig work condition and (2) subsequently leverage such design guidelines to build systems that digitally support platform-based laborers in their work. She also engages with regulators and policy experts to inform relevant policymaking.

Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control. Prior work has focused on human-AI interaction interface design and explainable AI research (XAI). However, despite these fruitful research results, why do many so-called “human-centered” AI systems still fail in the real world? In this talk, I will discuss the human-AI interaction paradigm, and show how we can learn from human-humancollaboration to design and build AI systems that lead to a successful interaction paradigm, especially in this LLM era. This work serves as a cornerstone towards the ultimate goal of human-AI collaboration, where AI and humans can take complementary and indispensable roles to achieve a better outcome and experience.

About the speaker
Dr. Dakuo Wang is an Associate Professor at Northeastern University. His research lies at the intersection of human-computer interaction(HCI), artificial intelligence (AI), and computer-supported team collaboration (CSCW), with a focus on the exploration, development, and evaluation of human-centered AI (HCAI) systems. The overarching research goal is to democratize AI for every person and every organization, so that they can easily access AI and collaboratewith AI to accomplish real-world tasks better — the “human-AI collaboration” paradigm. Before joining Northeastern, Dr.Wang worked as a research lead at IBM Research, and a principal investigator at MIT-IBM Watson AI Lab. He got his Ph.D. from the University of California Irvine (“how people write together now” co-advised by Judith Olson and Gary Olson). He has worked as a designer, researcher, and engineer in the U.S., China, and France. He has served in various organizing committees, program committees, and editorial boards for conferences and journals, and ACM has recognized him as an ACM Distinguished Speaker.

Welcome to the CSCW Paper Swap Workshop!

Are you submitting a paper to CSCW? Would you like feedback on it?

We’re doing this year on Friday, October 11th from 2:30pm -4:30 pm. Come for the hybrid meetup and share your papers whatever state they are in (including workshopping only individual parts of your paper)! Expect to read someone else’s paper during the swap time. Bring copies of your drafts.

We’ll plan roughly an hour per swap–30 minutes to read, 30 minutes for discussion and feedback. You can participate in as many or as few swap sessions as you’d like.

If you’re interested in attending, please “register” and let us know you are coming.

Not submitting to CSCW? Come anyway to read other people’s drafts, and get a sneak preview of the work coming out of this year!