Angie Zhang’s research while affiliated with University of Texas at Austin and other places

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Publications (14)


Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work
  • Preprint
  • File available

February 2025

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5 Reads

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Angie Zhang

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Sajel Surati

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[...]

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Haiyi Zhu

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 use 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.

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Fig. 1. Summary of Main Findings: Figure shows initiatives that policy domain experts and workers desired to support with data collected through a data-sharing system. Center of diagram demonstrates three shared initiatives between the stakeholder groups.
Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data-Sharing

December 2024

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25 Reads

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1 Citation

The proliferating adoption of platform-based gig work increasingly raises concerns for worker conditions. Past studies documented how platforms leveraged design to exploit labor, withheld information to generate power asymmetries, and left workers alone to manage logistical overheads as well as social isolation. However, researchers also called attention to the potential of helping workers overcome such costs via worker-led datasharing, which can enable collective actions and mutual aid among workers, while offering advocates, lawmakers and regulatory bodies insights for improving work conditions. To understand stakeholders' desiderata for a data-sharing system (i.e. functionality and policy initiatives that it can serve), we interviewed 11 policy domain experts in the U.S. and conducted co-design workshops with 14 active gig workers across four domains. Our results outline policymakers' prioritized initiatives, information needs, and (mis)alignments with workers' concerns and desires around data collectives. We offer design recommendations for data-sharing systems that support worker needs while bringing us closer to legislation that promote more thriving and equitable gig work futures.



Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities

September 2024

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18 Reads

Platform-based laborers face unprecedented challenges and working conditions that result from algorithmic opacity, insufficient data transparency, and unclear policies and regulations. The CSCW and HCI communities increasingly turn to worker data collectives as a means to advance related policy and regulation, hold platforms accountable for data transparency and disclosure, and empower the collective worker voice. However, fundamental questions remain for designing, governing and sustaining such data infrastructures. In this workshop, we leverage frameworks such as data feminism to design sustainable and power-aware data collectives that tackle challenges present in various types of online labor platforms (e.g., ridesharing, freelancing, crowdwork, carework). While data collectives aim to support worker collectives and complement relevant policy initiatives, the goal of this workshop is to encourage their designers to consider topics of governance, privacy, trust, and transparency. In this one-day session, we convene research and advocacy community members to reflect on critical platform work issues (e.g., worker surveillance, discrimination, wage theft, insufficient platform accountability) as well as to collaborate on codesigning data collectives that ethically and equitably address these concerns by supporting working collectivism and informing policy development.




Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

April 2023

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54 Reads

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45 Citations

Proceedings of the ACM on Human-Computer Interaction

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders---decision makers (faculty) and decision subjects (students)---use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.


Figure 2: Work Planner data probe that participants interacted with to view predictions of their schedules and surface design considerations.
Figure 3: One of the figures created using driver's individual data for the pilots. This uses their trip history to depict average earnings for the time and distance they drove in trips.
Figure 4: One of the figures created using driver's individual data for the pilots. This uses their trip history to plot each trip for its duration and distance so drivers can identify patterns or outliers of trips they drive.
Figure 5: Individual Data Probes
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

March 2023

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93 Reads

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers' own data as well as city-level data, we create probes -- interactive data visuals -- that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers' well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.


Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

February 2023

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146 Reads

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders -- decision makers (faculty) and decision subjects (students) -- use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.


Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities

January 2023

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54 Reads

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11 Citations

Accident Analysis & Prevention

In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind.


Citations (9)


... The need for sharing information and building solidarity among gig workers prompted calls from the HCI and CSCW communities to build worker-centered data collectives [12,29,44,45,100]. But despite the shared underlying challenges that hinder gig worker collectivism and the recognition among the research community of the necessity of collective data-sharing to inform evidence-driven policy initiatives, the gig workforce remains divided and segregated [45]. ...

Reference:

Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work
Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data-Sharing

... Recently, scholars identified worker-led data-sharing as a crucial step towards empowering the gig worker collective and advancing related policy [50,63,88]. Calacci advocated for Digital Workerism (worker-led data-driven research and design of governance tools to shift power back to the worker) [13], Zhang et. ...

Worker Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities
  • Citing Conference Paper
  • November 2024

... This large dataset contains around 300 million records from August 2018 to December 2022 and around 167 million records from January 2023 to the present 2 , averaging 200,000 to 300,000 records per day. It captures comprehensive aspects of ride-sharing trips and has been used in previous research to assess algorithm fairness [50,70]. ...

Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work
  • Citing Conference Paper
  • May 2024

... Studies documented two main ways that workers understand and manage work: on their own through self-tracking, or with peers via online groups/forums. Recent work at the intersection of HCI and Personal Informatics revealed how gig workers currently (or might in the future) self-track to (1) protect themselves from the platform [93] or customers [29,72] (2) comply with tax obligations [77,104] (3) understand how algorithms operate [29,120] and (4) comprehend and improve their own earning patterns [39,45,120] using tools such as data probes in addition to apps designed for tracking fuel, time, tax, mileage 1 and generalized gig work assistance [39]. For instance, Mystro a commercial tool affording rideshare drivers the agency to autodecline work across platforms that do not match their expressed preferences (e.g., earning rates, duration of gigs, work locations). ...

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
  • Citing Conference Paper
  • April 2023

... Contributions in this area span diverse fields, including healthcare [15], judicial systems [4], civic engagement [1], philanthropy [28], and urban planning [36]. More technical applications include collective debiasing [9], collaborative debugging [32], ranking with partial preferences [8] and web-based tools for democratizing ML workflows [46] . Collectively, these contributions reflect what has been described as a "participatory turn" in AI design [13]. ...

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
  • Citing Article
  • April 2023

Proceedings of the ACM on Human-Computer Interaction

... Based on these studies, it is evident that there is growing interest in exploring the effects of built environment on road safety using street view images and advanced computational methods. It is thus not surprising that streetscape elements have become a hot topic in safety research Haddad et al., 2023;Hamim and Ukkusuri, 2024). Despite these advancements, most existing research focuses on objective variables (for instance, the physical environment) derived from street view images. ...

Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities
  • Citing Article
  • January 2023

Accident Analysis & Prevention

... Rijo and Waldzus [248] looked into how voting patterns and political beliefs influence the way people evaluate information credibility. Jia et al. [141] focused on liberal and conservative users. Other works (e.g., [130]) focus on social media users in general. ...

Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
  • Citing Article
  • November 2022

Proceedings of the ACM on Human-Computer Interaction

... Algorithmic management in gig work raises significant fairness concerns in task allocation, wage determination, and worker evaluation [34,40,69]. Workers contend with a severe information asymmetry, as platforms control crucial details about demand and algorithmic rules, leaving them to infer decision-making processes from online forums and peer discussions [57]. ...

Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work

... Balancing Expert and Local Knowledge Efforts to integrate specialized and local knowledge can face disagreements over data validity, model interpretability, and ethical guidelines (Birhane et al., 2022). Translational strategies-ranging from interdisciplinary facilitation teams to community-guided metrics-can help mediate these gaps (Fischer, 2000;Lee et al., 2021). Table 1 are context-specific, some show that sustained grassroots advocacy can influence decision-making. ...

Reference:

The Right to AI
Participatory Algorithmic Management: Elicitation Methods for Worker Well-Being Models