Pith. sign in

REVIEW 2 major objections 4 minor 2 cited by

Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data-Sharing

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Gig workers and policymakers share data priorities: pay, discrimination, and safety are the common agenda for a worker-centered data-sharing system.

desk verdict A useful cross-stakeholder map of gig-worker data-sharing needs, with a real but fixable prompt-contamination concern in the main alignment finding. read the letter →

arxiv 2412.02973 v2 pith:PWNY3QAR submitted 2024-12-04 cs.CY

Jane Hsieh , Angie Zhang , Mialy Rasetarinera , Erik Chou , Daniel Ngo , Karen Lightman , Min Kyung Lee , Haiyi Zhu This is my paper · ORCID
classification cs.CY
keywords gigworkworkerdatasharingcollectivesfairpaysafetydiscriminationco-designpolicydesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that U.S. gig workers and policy experts, despite working on different sides of the table, largely agree on what a worker-centered data-sharing system should accomplish: both groups want data that exposes pay practices and unpaid work time, and both want stronger attention to discrimination and safety. On top of that shared ground, policy experts want to measure worker stress, while workers want to share practical strategies and experiences with each other. The paper argues that a data-sharing system designed around these shared initiatives, with workers retaining control over who sees their data, could support both worker advocacy and policy-making. If the authors are right, this gives a concrete design agenda for building gig-worker data collectives that go beyond tax-tracking apps and actually address labor exploitation.

What carries the argument

The argument is carried by a two-pronged qualitative protocol: semi-structured interviews with 11 U.S. policy domain experts and co-design workshops with 14 active gig workers across four domains (freelancing, food delivery, rideshare, and petsitting). The central proposed object is a worker-centered data-sharing system (a data collective) that aggregates pay, discrimination, and safety data alongside qualitative worker narratives, with workers controlling access. Iterative thematic coding of the transcripts produces the distinction between shared initiatives and stakeholder-specific priorities, which then drives the design recommendations.

What would settle it

Survey a large, representative sample of U.S. gig workers and policy professionals on their top priorities for worker data use; if pay, discrimination, and safety do not emerge as the top shared initiatives, the finding does not generalize. Alternatively, deploy a prototype data-sharing system in one gig domain and observe whether workers contribute data and whether policymakers use it to inform actual policy documents.

Watch

Extended reading notes

Core claim

The central claim is that both stakeholder groups sought 1) data to understand pay practices and (unpaid) work time and 2) more attention toward the issues of discrimination and safety. Workers and policy experts also showed distinct priorities: policy experts emphasized measuring worker stress, especially for caregivers and those juggling multiple roles, while workers prioritized the exchange of qualitative experiences and strategies. The paper concludes that these shared initiatives should anchor the design of worker-centered data-sharing systems, and that the system should balance worker control, data integrity, and multi-stakeholder governance to bring legislation closer to equitable gig work futures.

Load-bearing premise

The load-bearing premise is that the priorities voiced by 11 policy experts and 14 gig workers recruited through convenience sampling represent the broader population of U.S. gig workers and policy stakeholders.

Editorial extensions

If this is right

  • A data-sharing system that aggregates pay, discrimination, and safety data, and that also supports qualitative experience-sharing, could serve both worker advocacy and policy-making.
  • System designers should let workers control access (e.g., aggregate vs. individual data) while giving policymakers the summaries they need, since workers are wary of sharing raw individual data with government and peers alike.
  • Addressing data integrity is essential: workers' heterogeneous preferences for manual vs. automated uploads, plus off-app payments, can bias the data unless collection methods are designed carefully.
  • A data-sharing system should elevate worker-specific goals like experience-sharing even when they do not directly serve policy, because those features create the incentive for workers to participate.
  • Governance of such a system will likely require shared ownership among workers, advocacy groups, and neutral third parties, since participants voiced no consensus on a single owner.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the shared priorities are representative, a worker data cooperative could reasonably focus its first efforts on pay, safety, and bias metrics, leaving stress as a secondary data ask that policy experts may need to champion on their own.
  • The divergence between policy experts' focus on stress and workers' focus on experience-sharing suggests that real deployments will require negotiating data-collection scope; a pilot could test whether both types of data can be collected without burdening workers.
  • Because workers worried about data quality when manually uploading 'cherry-picked' good weeks, policy analyses built on worker-sourced data will likely have systematic blind spots unless hybrid collection methods (automatic plus prompted manual) are built in from the start.
  • The cross-domain differences observed among freelancers, delivery drivers, rideshare drivers, and petsitters imply that a shared system may need domain-specific data schemas and sharing preferences, which the current study maps qualitatively but does not yet quantify.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This paper reports a qualitative, two-stage study aimed at identifying the policy initiatives and data needs that a worker-centered data-sharing system for gig workers should support. The authors interviewed 11 U.S. policy domain experts (policymakers, policy implementers, advocates, and a policy researcher) and conducted co-design workshops with 14 U.S.-based gig workers across four domains (freelancing, food delivery, rideshare, petsitting). Thematic analysis of interviews and workshops produced 1593 codes, 118 first-level themes, 17 second-level themes, and four third-level themes. The central finding is that both stakeholder groups share priorities around pay practices and unpaid work time, discrimination and equity, and safety; the groups differ in that policy experts emphasize stress (especially for caregiving workers) while workers emphasize sharing qualitative work strategies and experiences. Based on these findings, the paper offers design recommendations for data-sharing systems, including features for data collection, education, and policy collaboration, and reflects on challenges of data integrity, invisible labor, and privacy.

Significance. If the central alignment finding is valid, this is a timely and useful contribution to the HCI/CSCW literature on gig work and data activism. The paper is transparent about its qualitative methodology, uses multiple coders, provides rich participant quotes, and grounds its design recommendations in participant preferences. The study also extends prior work by explicitly engaging both policy experts and workers to identify shared versus stakeholder-specific policy priorities. However, the significance of the contribution depends heavily on whether the reported shared priorities were genuinely elicited from participants rather than supplied by the researchers' materials. The paper currently does not provide the evidence needed to rule out that possibility, which is why a revision is needed.

major comments (2)
  1. [§3.2.2 and §3.2.1] The central claim that 'both stakeholder groups sought data to understand pay practices and (unpaid) work time and more attention toward the issues of discrimination and safety' may be partially an artifact of the research protocol. In §3.2.2, worker workshops asked participants to rank their own sticky notes 'alongside prepopulated notes,' and in §3.2.1, policy experts were handed a researcher-prepared list of potential data types divided by occupation and asked which would be useful. The paper does not report any analysis separating unprompted from prompted mentions. Because the alignment between stakeholders is the main empirical contribution, the absence of such a prompted-versus-unprompted analysis is load-bearing: it is possible that the reported shared initiatives (pay, discrimination, safety) were already present in the materials the research team brought into the room. I recommend that the authors reanalyze their data to distinguish between themes that arose spontaneously and those that appeared only after exposure to the researchers' prepopulated lists/notes, or at minimum report a sensitivity analysis that clearly describes which findings rely on prompted responses and which on unprompted ones. The limitation discussion in Section 6 does not address this issue.
  2. [§3.1.1 and §4.2] The policy expert group is heterogeneous, comprising city-level policymakers, county and federal implementers, advocacy group representatives, and a policy researcher. The paper treats these as a single 'policy domain expert' group and does not report analyses separated by role. The authors note in Section 6 that they did not separate by governing level, but they also do not separate by role type. Given that advocacy groups and academic researchers may have different policy priorities than elected officials or agency implementers, the reported 'policy expert' perspective could obscure meaningful variation. This does not invalidate the study, but it limits the specificity of the claims about what 'policymakers' want. The authors should either provide a subgroup breakdown or add a clearer caveat about the heterogeneity of this group.
minor comments (4)
  1. [Table 2 and §4.1.1] Table 2 lists three petsitting participants (W1, W2, W3), but the text in §4.1.1 quotes a participant 'W5' regarding ethnic names and discrimination; please reconcile the participant IDs or correct the reference.
  2. [§3.3] The text 'our prior experiencedoes not remedy' is missing a space between 'experience' and 'does'; please fix the typographical error.
  3. [§3.4] The coding process is described, but no inter-rater reliability metrics are reported. For a qualitative study, agreement metrics are not always required, but reporting them (or a rationale for not using them) would strengthen confidence in the 1593-code thematic structure.
  4. [§3.2.2] The paper mentions that the full protocol and study materials are in supplementary materials, but the arXiv version does not include them. Please provide the supplementary materials or describe the workshop protocol in enough detail for replication.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is an empirical qualitative study, and the prompted-materials concern is a transparent internal-validity limitation, not a derivational reduction.

full rationale

This paper is an empirical qualitative study (11 policy-expert interviews, 14 gig-worker co-design workshops) that reports thematic findings; it makes no formal derivation, fits no parameters, and offers no prediction from a model. The central finding that both stakeholder groups prioritize pay/unpaid time, discrimination/equity, and safety is a thematic summary of coded interviews and workshops. The closest circularity-adjacent element is the study design: worker workshops used prepopulated sticky notes and introduced initiatives 'informed by the previous interviews' (§3.2.2), while policy experts reacted to a researcher-prepared data-type list (§3.2.1). This is a genuine internal-validity and potential priming limitation, and the paper describes it transparently and attempts to mitigate priming by introducing worker-framed initiatives only at the end of the sessions. However, priming does not make the reported themes logically equivalent to the researchers' inputs by construction: the analysis was bottom-up open coding of transcripts, participants generated additional items beyond the prepopulated materials, and the paper explicitly acknowledges representation and design limitations in §6. Self-citations appear as related-work background and as sources of sample questions, but they are not load-bearing in any derivational sense. Under the requested circularity definitions, no specific circular step can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numerical or free parameters. The claims rest on domain assumptions about participant candor, coding validity, and sample representativeness, plus the representativeness of four gig domains. No new physical or technical entities are introduced.

assumptions (3)
  • domain assumption Participant self-reports in interviews and workshops reflect their actual policy and data needs.
    All findings about priorities depend on participants being candid and accurate; there is no independent verification of stated concerns. Invoked throughout Section 4.
  • domain assumption Two-coder qualitative thematic analysis yields reliable themes from transcripts.
    Section 3.4 describes one researcher generating codes and at least one other cross-checking, but no inter-rater reliability metric is reported; the 118 first-level themes are an interpretive construction.
  • domain assumption A sample of 11 policy experts and 14 workers across four gig domains is adequate to map stakeholder desiderata for the U.S.
    Section 3.1 and Section 6 acknowledge the sample is small and non-random; if it is unrepresentative, the recommended priorities may not generalize.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data-Sharing." pith.science (2026). https://pith.science/paper/PWNY3QAR

@misc{pith2026241202973,
  author       = {Pith},
  title        = {Pith review of: Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data-Sharing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PWNY3QAR}},
  note         = {Machine review of arXiv:2412.02973}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2412.02973 by the authors.

Figure 1
Figure 1. Summary of Main Findings: Figure shows initiatives that policy domain experts and workers desired to support with data [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work

    cs.HC 2025-02 conditional novelty 6.0 of 10

    A 7-day field study with 14 gig workers found that a cross-platform data-sharing tool supports mutual support, financial reflection, and worker willingness to share data with policymakers.

  2. Digital Labor: Challenges, Ethical Insights, and Implications

    cs.HC 2025-06 conditional novelty 4.0 of 10

    A review of 143 papers from 2015 to 2024 finds digital gig workers are underpaid, invisible, and underrepresented in both platforms and the research about them, and it names the gaps researchers should fill next.

Reference graph

Works this paper leans on

111 extracted references · 39 canonical work pages · cited by 2 Pith papers

  1. [1]

    2021. WeClock. https://weclock.it/

    2021

  2. [2]

    2019.Individual and Collaborative Behaviors of Rideshare Drivers in Protecting their Safety

    Mashael Yousef Almoqbel and Donghee Yvette Wohn. 2019.Individual and Collaborative Behaviors of Rideshare Drivers in Protecting their Safety. New York, NY, USA. https://doi.org/10.1145/3359319

    work page doi:10.1145/3359319 2019

  3. [3]

    Mohammad Amir Anwar and Mark Graham. 2021. Between a rock and a hard place: Freedom, flexibility, precarity and vulnerability in the gig economy in Africa. Competition & Change 25, 2 (2021), 237–258. https://doi.org/10.1177/1024529420914473

    doi:10.1177/1024529420914473 2021

  4. [4]

    Uttam Bajwa, Denise Gastaldo, Erica Di Ruggiero, and Lilian Knorr. 2018. The health of workers in the global gig economy. Global. Health 14, 1 (Dec. 2018), 124. https://doi.org/10.1186/s12992-018-0444-8

    doi:10.1186/s12992-018-0444-8 2018

  5. [5]

    Richard A Bales and Christian Patrick Woo. 2017. The Uber million dollar question: Are Uber drivers employees or independent contractors. (2017). https://ssrn.com/abstract=2759886

    2017

  6. [6]

    Subhashis Basu, Giles Ratcliffe, and Mark Green. 2015. Health and pink-collar work. Occupational Medicine 65, 7 (2015), 529–534

    2015

  7. [7]

    Annette Bernhardt, Ruth Milkman, Nik Theodore, Douglas D Heckathorn, Mirabai Auer, James DeFilippis, Ana Luz González, Victor Narro, Jason Perelshteyn, Diana Polson, et al. 2010. Broken laws, unprotected workers. (2010). https://www.nelp.org/publication/broken-laws-unprotected- workers-violations-of-employment-and-labor-laws-in-americas-cities/

    2010

  8. [8]

    Hugh Beyer and Karen Holtzblatt. 1998. Contextual Design: Defining Customer-Centered Systems. Morgan Kaufmann

    1998

Show all 111 references
  1. [9]

    Matthew Bietz, Kevin Patrick, and Cinnamon Bloss. 2019. Data donation as a model for citizen science health research. Citizen Science: Theory and Practice 4, 1 (2019). https://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.178

    2019 doi

  2. [10]

    Allie Blaising, Yasmine Kotturi, Chinmay Kulkarni, and Laura Dabbish. 2021. Making it Work, or Not: A Longitudinal Study of Career Trajectories Among Online Freelancers. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 226 (jan 2021), 29 pages. https://doi.org/10.1145/3432925

    2021 doi

  3. [11]

    Laura Boeschoten, Niek C de Schipper, Adriënne M Mendrik, Emiel van der Veen, Bella Struminskaya, Heleen Janssen, and Theo Araujo. 2023. Port: A software tool for digital data donation. Journal of Open Source Software 8, 90 (2023), 5596. https://doi.org/10.21105/joss.05596

    2023 doi

  4. [12]

    L Branstetter, B Li, and L Taylor. 2020. Can ridesharing help the disadvantaged get moving? (2020). https://ppms.cit.cmu.edu/media/project_files/ 170_-_Final_Report.pdf cited 11 Sep 2023

    2020

  5. [14]

    Dan Calacci and Alex Pentland. 2022. Bargaining with the Black-Box: Designing and Deploying Worker-Centric Tools to Audit Algorithmic Management. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 428 (nov 2022), 24 pages. https://doi.org/10.1145/3570601

    2022 doi

  6. [15]

    Thijs C Carrière, Laura Boeschoten, Bella Struminskaya, Heleen Janssen, Niek C de Schipper, and Theo Araujo. 2023. Best practices in data donation: A workflow for studies using digital data donation. (Oct 2023). https://doi.org/10.31219/osf.io/3vhbj

    2023 doi

  7. [16]

    John M Carroll. 2010. Synthesis Lectures on Human-Centered Informatics. https://www.springer.com/series/16906

    2010

  8. [17]

    Lee, Bongshin Lee, Wanda Pratt, and Julie A

    Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, and Julie A. Kientz. 2014. Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada)...

    2014

  9. [18]

    Nicola Christie and Heather Ward. 2019. The health and safety risks for people who drive for work in the gig economy. Journal of Transport & Health 13 (2019), 115–127. https://doi.org/10.1016/j.jth.2019.02.007

    2019 doi

  10. [19]

    Ruth Berins Collier, Veena Dubal, and Christopher Carter. 2017. Labor platforms and gig work: the failure to regulate. (2017). https://doi.org/10. 2139/ssrn.3039742

    2017

  11. [20]

    Ruth Berins Collier, Veena B Dubal, and Christopher L Carter. 2018. Disrupting regulation, regulating disruption: The politics of Uber in the United States. Perspectives on Politics 16, 4 (2018), 919–937. https://doi.org/10.1017/S1537592718001093

    2018 doi

  12. [21]

    Kate Conger and Kellen Browning. 2021. A judge declared California’s gig worker law unconstitutional. Now what? The New York Times (2021). https://www.nytimes.com/2021/08/23/technology/california-gig-worker-law-explained.html

    2021

  13. [22]

    W Alec Cram, Martin Wiener, Monideepa Tarafdar, and Alexander Benlian. 2022. Examining the impact of algorithmic control on Uber drivers’ technostress. Journal of management information systems 39, 2 (2022), 426–453. https://doi.org/10.1080/07421222.2022.2063556

    2022

  14. [23]

    W Alec Cram, Martin Wiener, Monideepa Tarafdar, Alexander Benlian, et al. 2020. Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior. InICIS. https://aisel.aisnet.org/icis2020/is_workplace_fow/is_workplace_fow/1 Manuscript submitted to ACM 26 Hsie...

    2020

  15. [24]

    Samantha Dalal, Ngan Chiem, Nikoo Karbassi, Yuhan Liu, and Andrés Monroy-Hernández. 2023. Understanding Human Intervention in the Platform Economy: A case study of an indie food delivery service. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (...

    2023

  16. [25]

    Valerio De Stefano. 2015. The rise of the just-in-time workforce: On-demand work, crowdwork, and labor protection in the gig-economy. Comparative Labor Law & Policy Journal 37 (2015), 471. https://doi.org/10.2139/ssrn.2682602

    2015 doi

  17. [26]

    Valerio De Stefano. 2018. The gig economy and labour regulation: an international and comparative approach. Law Journal of Social and Labor Relations 4 (2018), 68. Issue 2. https://doi.org/10.26843/mestradodireito.v4i2.158

    2018 doi

  18. [27]

    Valerio De Stefano, Ilda Durri, Charalampos Stylogiannis, and Mathias Wouters. 2021. Platform work and the employment relationship. Technical Report 27. https://www.ilo.org/static/english/intserv/working-papers/wp027/index.html

    2021

  19. [28]

    Dillahunt, Jason Lam, Alex Lu, and Earnest Wheeler

    Tawanna R. Dillahunt, Jason Lam, Alex Lu, and Earnest Wheeler. 2018. Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study. In Proceedings of the 2018 Designing Interactive Systems Conference (Hong Kong, China) (DIS ’18). Association for Co...

    2018

  20. [29]

    Kimberly Do, Maya De Los Santos, Michael Muller, and Saiph Savage. 2024. Designing Sousveillance Tools for Gig Workers. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA. https://do...

    2024

  21. [30]

    Sarah A Donovan, David H Bradley, and Jon O Shimabukuru. 2016. What does the gig economy mean for workers? https://sgp.fas.org/crs/misc/ R44365.pdf Available online, cited 13 Sep 2023

    2016

  22. [31]

    VB Dubal. 2017. The Drive to Precarity: A Political History of Work, Regulation, & Labor Advocacy in San Francisco’s Taxi & Uber Economies. Berkeley Journal of Employment and Labor Law 38 (2017), 73–135. https://ssrn.com/abstract=2921486

    2017

  23. [32]

    Veena Dubal. 2023. On algorithmic wage discrimination. Columbia Law Review 123, 7 (2023), 1929–1992

    2023

  24. [33]

    Alpana Dubey, Kumar Abhinav, Mary Hamilton, and Alex Kass. 2017. Analyzing Gender Pay Gap in Freelancing Marketplace. InProceedings of the 2017 ACM SIGMIS Conference on Computers and People Research (Bangalore, India) (SIGMIS-CPR ’17). Association for Computing Machinery, New ...

    2017

  25. [34]

    Michael Dunn, Isabel Munoz, and Steve Sawyer. 2021. Gender differences and lost flexibility in online freelancing during the COVID-19 pandemic. Frontiers in Sociology 6 (2021), 738024. https://doi.org/10.3389/fsoc.2021.738024

    2021

  26. [35]

    Benjamin G Edelman and Michael Luca. 2014. Digital discrimination: The case of Airbnb. com. Technical Report 14-054. https://doi.org/10.2139/ ssrn.2377353

    2014

  27. [36]

    Christy England. 2021. The National Institute for Workers’ Rights Advancing workers’ rights through research, thought leadership, and education for policymakers, advocates, and the public. (2021). https://niwr.org/2021/08/11/old-boundaries-new-horizons/

    2021

  28. [37]

    Epstein, An Ping, James Fogarty, and Sean A

    Daniel A. Epstein, An Ping, James Fogarty, and Sean A. Munson. 2015. A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp ’15). Association for Computing M...

    2015

  29. [38]

    Frances Flanagan. 2019. Theorising the gig economy and home-based service work. Journal of Industrial Relations 61, 1 (2019), 57–78. https: //doi.org/10.1177/0022185618800518

    2019 doi

  30. [39]

    Eureka Foong and Elizabeth Gerber. 2021. Understanding Gender Differences in Pricing Strategies in Online Labor Marketplaces. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New Y...

    2021

  31. [40]

    Gerald Friedman. 2014. Workers without employers: shadow corporations and the rise of the gig economy. Review of keynesian economics 2, 2 (2014), 171–188. https://doi.org/10.4337/roke.2014.02.03

    2014 doi

  32. [41]

    Mary Geschwindt. 2022. Biking is Labor: App-Based Food Delivery Cyclists and Infrastructure as Justice in New York City. Ph. D. Dissertation. Harvard University. https://dash.harvard.edu/handle/1/37371653

    2022

  33. [42]

    Mark Graham, Jamie Woodcock, Richard Heeks, Paul Mungai, Jean-Paul Van Belle, Darcy du Toit, Sandra Fredman, Abigail Osiki, Anri van der Spuy, and Six M Silberman. 2020. The Fairwork Foundation: Strategies for improving platform work in a global context. Geoforum 112 (2020), 1...

    2020 doi

  34. [43]

    Mary L Gray and Siddharth Suri. 2019. Ghost work: How to stop Silicon Valley from building a new global underclass. Eamon Dolan Books

    2019

  35. [44]

    Ashish Gupta, Tavishi Tewary, and Badri Narayanan Gopalakrishnan. 2022. Sustainability in the Gig Economy. Springer

    2022

  36. [45]

    Independent Worker

    Seth D Harris and Alan B Krueger. 2015. A Proposal for Modernizing Labor Laws for Twenty-First-Century Work: The" Independent Worker". Brookings Washington, DC

    2015

  37. [46]

    Adrian John Hawley. 2018. Regulating labour platforms, the data deficit. European Journal of Government and Economics 7, 1 (2018), 5–23. https://doi.org/10.17979/ejge.2018.7.1.4330%0A

    2018 doi

  38. [47]

    Pamela Hopwood, Ellen MacEachen, Ivy Bourgeault, Carrie McAiney, Basak Yanar, and Abbey Davis. 2024. On-Demand and Marketplace Platforms: Gig Care Work Conditions on Two Digital Labour Platform Care Models. Critical Sociology (2024), 08969205241279868

    2024

  39. [48]

    Jane Hsieh, Oluwatobi Adisa, Sachi Bafna, and Haiyi Zhu. 2023. Designing Individualized Policy and Technology Interventions to Improve Gig Work Conditions. InProceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work (Oldenburg, Germany) (CHI...

    2023

  40. [49]

    Jane Hsieh, Miranda Karger, Lucas Zagal, and Haiyi Zhu. 2023. Co-Designing Alternatives for the Future of Gig Worker Well-Being: Navigating Multi-Stakeholder Incentives and Preferences. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, US...

    2023

  41. [50]

    Jane Hsieh, Angie Zhang, Seyun Kim, Varun Nagaraj Rao, Samantha Dalal, Alexandra Mateescu, Rafael Do Nascimento Grohmann, Motahhare Eslami, and Haiyi Zhu. 2024. Worker Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities. In Compan...

    2024

  42. [51]

    Hannah Johnston, Chris Land-Kazlauskas, et al. 2018. Organizing on-demand: Representation, voice, and collective bargaining in the gig economy. ILO Working Papers (2018). https://www.ilo.org/publications/organizing-demand-representation-voice-and-collective-bargaining-gig

    2018

  43. [52]

    Zoe Kahn, Meyebinesso Farida Carelle Pere, Emily Aiken, Nitin Kohli, and Joshua E Blumenstock. 2024. Expanding Perspectives on Data Privacy: Insights from Rural Togo. arXiv preprint arXiv:2409.17578 (2024)

    2024 arXiv

  44. [53]

    Ria Kasliwal. 2020. Gender and the gig economy: A qualitative study of gig platforms for women workers. ORF Issue Brief 359 (2020), 1–14

    2020

  45. [54]

    Dara Kerr. 2022. More Than 350 Gig Workers Carjacked, 28 Killed, Over the Last Five Years–The Markup. https://themarkup.org/working-for- an-algorithm/2022/07/28/more-than-350-gig-workers-carjacked-28-killed-over-the-last-five-years

    2022

  46. [55]

    Florian Keusch, Paulina K Pankowska, Alexandru Cernat, and Ruben L Bach. 2023. Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation. Field Methods (2023). https://doi.org/10.1177/1525822X231225907

    2023 doi

  47. [56]

    Vera Khovanskaya, Lynn Dombrowski, Jeffrey Rzeszotarski, and Phoebe Sengers. 2019. The Tools of Management: Adapting Historical Union Tactics to Platform-Mediated Labor. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 208 (nov 2019), 22 pages. https://doi.org/10.1145/3359310

    2019 doi

  48. [57]

    SM Kisner and EL Jenkins. 1998. Niosh alert: Preventing worker injuries and deaths from traffic-related motor vehicle crashes. https: //www.cdc.gov/niosh/docs/98-142/default.html Report No.: DHHS (NIOSH) Publication

    1998

  49. [58]

    Jorn Kloostra. 2022. Algorithmic pricing: A concern for platform workers? European Labour Law Journal 13, 1 (2022), 108–126

    2022

  50. [59]

    Zoltan Kmetty, Ádám Stefkovics, Julia Szamely, Deng Dongning, Anikó Kellner, Elisa Omodei, Pauló Edit, and Júlia Koltai. 2023. Determinants of willingness to donate data from social media platforms. (2023). https://doi.org/10.17203/KDK584

    2023 doi

  51. [60]

    Weiwen Leung, Zheng Zhang, Daviti Jibuti, Jinhao Zhao, Maximilian Klein, Casey Pierce, Lionel Robert, and Haiyi Zhu. 2020. Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases. InProceedings of the 2020 CHI Conference on Human Factors in Computi...

    2020

  52. [61]

    Hanlin Li, Nicholas Vincent, Stevie Chancellor, and Brent Hecht. 2023. The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chic...

    2023

  53. [62]

    Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for Computing Machinery, New York, NY, USA, 557–566. ht...

    2010

  54. [63]

    Toby Jia-Jun Li, Yuwen Lu, Jaylexia Clark, Meng Chen, Victor Cox, Meng Jiang, Yang Yang, Tamara Kay, Danielle Wood, and Jay Brockman. 2022. A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality. In Proceedings of the 1st Annual Meetin...

    2022

  55. [64]

    Liang, Sean A

    Calvin A. Liang, Sean A. Munson, and Julie A. Kientz. 2021. Embracing Four Tensions in Human-Computer Interaction Research with Marginalized People. ACM Trans. Comput.-Hum. Interact. 28, 2, Article 14 (apr 2021), 47 pages. https://doi.org/10.1145/3443686

    2021 doi

  56. [65]

    Brush it Off

    Ning F. Ma, Veronica A. Rivera, Zheng Yao, and Dongwook Yoon. 2022. “Brush it Off”: How Women Workers Manage and Cope with Bias and Harassment in Gender-agnostic Gig Platforms. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA...

    2022

  57. [66]

    Attila Marton and Hamid R. Ekbia. 2019. The Political Gig-Economy: Platformed Work and Labour. In ICIS 2019 Proceedings. https://aisel.aisnet. org/icis2019/crowds_social/crowds_social/16

    2019

  58. [67]

    Sharon H Mastracci. 2016. Breaking out of the pink-collar ghetto: Policy solutions for non-college women. Routledge

    2016

  59. [68]

    Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019. Reliability and Inter-rater Reliability in Qualitative Research: Norms and Guidelines for CSCW and HCI Practice. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 72 (nov 2019), 23 pages. https://doi.org/10.1145/3359174

    2019 doi

  60. [69]

    Michelle N Meyer, John Basl, David Choffnes, Christo Wilson, and David MJ Lazer. 2023. Enhancing the ethics of user-sourced online data collection and sharing. Nature Computational Science 3, 8 (2023), 660–664. https://doi.org/10.1038/s43588-023-00490-7

    2023 doi

  61. [70]

    Kyzyl Monteiro, Yuchen Wu, and Sauvik Das. 2024. Manipulate to Obfuscate: A Privacy-Focused Intelligent Image Manipulation Tool for End-Users. In Adjunct Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology. 1–3

    2024

  62. [71]

    George Morgan and Pariece Nelligan. 2018. The creativity hoax: Precarious work in the gig economy. Anthem Press

    2018

  63. [72]

    Tonia Novitz. 2020. The potential for international regulation of gig economy issues. King’s Law Journal 31, 2 (2020), 275–286. https://doi.org/10. 1080/09615768.2020.1789442

    2020

  64. [73]

    Michael Quinn Patton. 1990. Qualitative evaluation and research methods. 2 (1990), 532

    1990

  65. [74]

    Michael Quinn Patton. 2014. Qualitative Research & Evaluation Methods: Integrating Theory and Practice. SAGE Publications

    2014

  66. [75]

    Caroline Criado Perez. 2019. Invisible women: Data bias in a world designed for men. Abrams. Manuscript submitted to ACM 28 Hsieh & Zhang et al

    2019

  67. [76]

    Edouard Pignot. 2023. Who is pulling the strings in the platform economy? Accounting for the dark and unexpected sides of algorithmic control. Organization 30, 1 (2023), 140–167

    2023

  68. [77]

    Noopur Raval and Paul Dourish. 2016. Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (San Francisco, California, USA) (CSCW ’16). As...

    2016

  69. [78]

    Wisniewski

    Afsaneh Razi, Ashwaq Alsoubai, Seunghyun Kim, Nurun Naher, Shiza Ali, Gianluca Stringhini, Munmun De Choudhury, and Pamela J. Wisniewski

  70. [79]

    Aditya Prasad Sahoo, Anish Patnaik, BCM Patnaik, and Ipseeta Satpathy. 2024. The Relationship between Financial Literacy and Investment Strategies among Gig Workers. In Synergy of AI and Fintech in the Digital Gig Economy. CRC Press, 199–216

    2024

  71. [80]

    Shruti Sannon and Dan Cosley. 2022. Toward a More Inclusive Gig Economy: Risks and Opportunities for Workers with Disabilities. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 335 (nov 2022), 31 pages. https://doi.org/10.1145/3555755

    2022 doi

  72. [81]

    Shruti Sannon, Billie Sun, and Dan Cosley. 2022. Privacy, surveillance, and power in the gig economy. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–15

    2022

  73. [82]

    Saiph Savage. 2024. Unveiling AI-Driven Collective Action for a Worker-Centric Future. InProceedings of the 17th ACM International Conference on Web Search and Data Mining. 6–7

    2024

  74. [83]

    Lindsey Schwartz and Nic Weber. 2023. Asymmetric by Design: How and Why Labor Policy Impacts Gig Workers. (2023). https://doi.org/10. 31235/osf.io/mh29a

    2023

  75. [84]

    Robert W Service. 2009. Book Review: Corbin, J., & Strauss, A.(2008). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . Thousand Oaks, CA: Sage. Organizational Research Methods 12, 3 (2009), 614–617

    2008

  76. [85]

    Riyaj Shaikh, Airi Lampinen, and Barry Brown. 2023. The Work to Make Piecework Work: An Ethnographic Study of Food Delivery Work in India During the COVID-19 Pandemic. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 243 (oct 2023), 23 pages. https://doi.org/10.1145/3610034

    2023 doi

  77. [86]

    Aaron Shaw, Floor Fiers, and Eszter Hargittai. 2023. Participation inequality in the gig economy. Information, Communication & Society 26, 11 (2023), 2250–2267

    2023

  78. [87]

    Riordan, Hye Jin Rho, Chinmay Kulkarni, Marlen Martinez-Lopez, Betsy Stringam, Ben Begleiter, and Jodi Forlizzi

    Franchesca Spektor, Sarah E Fox, Ezra Awumey, Christine A. Riordan, Hye Jin Rho, Chinmay Kulkarni, Marlen Martinez-Lopez, Betsy Stringam, Ben Begleiter, and Jodi Forlizzi. 2023. Designing for Wellbeing: Worker-Generated Ideas on Adapting Algorithmic Management in the Hospitali...

    2023

  79. [88]

    Jake M L Stein, Vidminas Vizgirda, Max Van Kleek, Reuben Binns, Jun Zhao, Rui Zhao, Naman Goel, George Chalhoub, Wael S Albayaydh, and Nigel Shadbolt. 2023. ‘You are you and the app. There’s nobody else. ’: Building Worker-Designed Data Institutions within Platform Hegemony. I...

    2023

  80. [89]

    Andrew Stewart and Jim Stanford. 2017. Regulating work in the gig economy: What are the options? The Economic and Labour Relations Review 28, 3 (2017), 420–437. https://doi.org/10.1177/1035304617722461

    2017 doi

  81. [90]

    Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. (1998)

    1998

  82. [91]

    Veronika Strotbaum, Monika Pobiruchin, Björn Schreiweis, Martin Wiesner, and Brigitte Strahwald. 2019. Your data is gold–Data donation for better healthcare? It-Information Technology 61, 5-6 (2019), 219–229. https://doi.org/10.1515/itit-2019-0024

    2019 doi

  83. [92]

    Zephyr Teachout. 2023. Algorithmic Personalized Wages. Politics & Society 51, 3 (2023), 436–458

    2023

  84. [93]

    Julia Ticona. 2022. Left to our own devices: Coping with insecure work in a digital age. Oxford University Press

    2022

  85. [94]

    Julia Ticona, Alexandra Mateescu, and Alex Rosenblat. 2018. Beyond disruption: How tech shapes labor across domestic work and ridehailing. (2018)

    2018

  86. [95]

    Carlos Toxtli, Siddharth Suri, and Saiph Savage. 2021. Quantifying the Invisible Labor in Crowd Work. Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 319 (oct 2021), 26 pages. https://doi.org/10.1145/3476060

    2021 doi

  87. [96]

    Molly Tran and Rosemary K Sokas. 2017. The gig economy and contingent work: An occupational health assessment. Journal of occupational and environmental medicine 59, 4 (2017), e63. https://doi.org/10.1097/JOM.0000000000000977

    2017 doi

  88. [97]

    Steven P Vallas. 2019. Platform capitalism: what’s at stake for workers?. In New Labor Forum, Vol. 28. SAGE Publications Sage CA: Los Angeles, CA, 48–59

    2019

  89. [98]

    Niels Van Doorn. 2020. From wage to a wager: Dynamic pricing in the gig economy. Platform Equality (2020)

    2020

  90. [99]

    Niels Van Doorn, Fabian Ferrari, and Mark Graham. 2023. Migration and migrant labour in the gig economy: An intervention. Work, Employment and Society 37, 4 (2023), 1099–1111

    2023

  91. [100]

    Aditya Vashistha, Richard Anderson, and Shrirang Mare. 2018. Examining security and privacy research in developing regions. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. 1–14

    2018

  92. [101]

    Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, and Brent Hecht. 2021. Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Vir...

    2021

  93. [102]

    Alex J Wood, Mark Graham, Vili Lehdonvirta, and Isis Hjorth. 2019. Good gig, bad gig: autonomy and algorithmic control in the global gig economy. Work, employment and society 33, 1 (2019), 56–75. https://doi.org/10.1177/095001701878561

    2019 doi

  94. [103]

    Jamie Woodcock and Mark Graham. 2019. The gig economy. A critical introduction. Cambridge: Polity (2019), 54. http://acdc2007.free.fr/ woodcock2020.pdf

    2019

  95. [104]

    Philip F Wu, Ruoshu Zheng, Ying Zhao, and Yixi Li. 2022. Happy riders are all alike? Ambivalent subjective experience and mental well-being of food-delivery platform workers in China. New Technology, Work and Employment 37, 3 (2022), 425–444. https://doi.org/10.1111/ntwe.12243

    2022 doi

  96. [105]

    Deepika Yadav, Kasper Karlgren, Riyaj Shaikh, Karey Helms, Donald Mcmillan, Barry Brown, and Airi Lampinen. 2024. Bodywork at Work: Attending to Bodily Needs in Gig, Shift, and Knowledge Work. In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu...

    2024

  97. [106]

    Zheng Yao. 2024. Peer Support in Online Communities. Ph. D. Dissertation. Carnegie Mellon University. http://reports-archive.adm.cs.cmu.edu/ anon/hcii/CMU-HCII-24-107.pdf#page=81.17

    2024

  98. [107]

    Zheng Yao, Silas Weden, Lea Emerlyn, Haiyi Zhu, and Robert E. Kraut. 2021. Together But Alone: Atomization and Peer Support among Gig Workers. Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 391 (oct 2021), 29 pages. https://doi.org/10.1145/3479535

    2021 doi

  99. [108]

    Savvas Zannettou, Olivia-Nemes Nemeth, Oshrat Ayalon, Angelica Goetzen, Krishna P Gummadi, Elissa M Redmiles, and Franziska Roesner. 2023. Leveraging rights of data subjects for social media analysis: Studying TikTok via data donations. arXiv preprint (2023). https://doi.org/1...

    2023 doi arXiv

  100. [109]

    Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany...

    2023

  101. [110]

    Angie Zhang, Alexander Boltz, Chun Wei Wang, and Min Kyung Lee. 2022. Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (C...

    2022

  102. [111]

    Angie Zhang, Rocita Rana, Alexander Boltz, Veena Dubal, and Min Kyung Lee. 2024. Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work. In Proceedings of the CHI Conferen...

    2024

  103. [2022]

    In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22)

    Instagram Data Donation: A Case Study on Collecting Ecologically Valid Social Media Data for the Purpose of Adolescent Online Risk Detection. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22). Association...

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.