[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3533406.3533418acmotherconferencesArticle/Chapter ViewAbstractPublication PageschiworkConference Proceedingsconference-collections
research-article

A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality

Published: 08 June 2022 Publication History

Abstract

The growing inequality in gig work between workers and platforms has become a critical social issue as gig work plays an increasingly prominent role in the future of work. The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers’ diverse preferences, and workers’ lack of trust in the platforms. In this position paper, we argue that a bottom-up approach that empowers individual workers to access AI-enabled work planning support and share data among a group of workers through a network of end-user-programmable intelligent assistants is a practical way to bridge AI inequality in gig work under the current paradigm of privately owned platforms. This position paper articulates a set of research challenges, potential approaches, and community engagement opportunities, seeking to start a dialogue on this important research topic in the interdisciplinary CHIWORK community.

References

[1]
[1] Chicago Rideshare Advocates.2022. https://www.facebook.com/chicagorideshareadvocates/
[2]
James F. Allen, Curry I Guinn, and Eric Horvtz. 1999. Mixed-initiative interaction. IEEE Intelligent Systems and their Applications 14, 5(1999), 14–23.
[3]
[3] Chicago Gig Alliance.2022. https://chicagogigalliance.org/
[4]
Mark Andrejevic. 2014. Big data, big questions| the big data divide. International Journal of Communication 8 (2014), 17.
[5]
Arcade City Austin. 2022. Arcade City Austin. https://www.facebook.com/groups/ArcadeCityAustin/about/
[6]
R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilović, S. Nagar, K. Natesan Ramamurthy, J. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, and Y. Zhang. 2019. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development 63, 4/5 (2019), 4:1–4:15. https://doi.org/10.1147/JRD.2019.2942287
[7]
Dan Calacci. 2022. Gigbox. https://gigbox.media.mit.edu/app/
[8]
Dan Calacci and Alex (Sandy) Pentland. 2022. Bargaining With The Black-Box Designing and Deploying Worker-Centric Tools to Audit Algorithmic Management. In Preprint.
[9]
Lindsey Cameron, Angele Christin, Michael Ann DeVito, Tawanna R. Dillahunt, Madeleine Elish, Mary Gray, Rida Qadri, Noopur Raval, Melissa Valentine, and Elizabeth Anne Watkins. 2021. “This Seems to Work”: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor. ACM, New York, NY, USA. https://doi.org/10.1145/3411763.3441331
[10]
Xinyu Cao, Dennis Zhang, and Lei Huang. 2020. The impact of COVID-19 pandemic on gig economy labor supply. NYU Stern School of Business(2020).
[11]
Abhijnan Chakraborty, Aniko Hannak, Asia J Biega, and Krishna Gummadi. 2017. Fair sharing for sharing economy platforms. In Fairness, Accountability and Transparency in Recommender Systems-Workshop on Responsible Recommendation.
[12]
Driver’s Seat Cooperative. 2022. Driver’s Seat Cooperative. https://driversseat.co/
[13]
The Drivers Cooperative. 2022. The Drivers Cooperative. https://drivers.coop
[14]
Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, and Andrés Monroy-Hernández. 2017. Calendar.Help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). ACM, New York, NY, USA, 2382–2393. https://doi.org/10.1145/3025453.3025780
[15]
Allen Cypher and Daniel Conrad Halbert. 1993. Watch what I do: programming by demonstration. MIT press.
[16]
Sylvie Delacroix and Neil D Lawrence. 2019. Bottom-up data Trusts: disturbing the ‘one size fits all’approach to data governance. International data privacy law 9, 4 (2019), 236–252.
[17]
Tawanna R. Dillahunt, Sheena Erete, Roxana Galusca, Aarti Israni, Denise Nacu, and Phoebe Sengers. 2017. Reflections on Design Methods for Underserved Communities. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing(Portland, Oregon, USA) (CSCW ’17 Companion). ACM, New York, NY, USA, 409–413. https://doi.org/10.1145/3022198.3022664
[18]
Tawanna R. Dillahunt and Amelia R. Malone. 2015. The Promise of the Sharing Economy among Disadvantaged Communities. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). ACM, New York, NY, USA, 2285–2294. https://doi.org/10.1145/2702123.2702189
[19]
Tawanna R. Dillahunt, Xinyi Wang, Earnest Wheeler, Hao Fei Cheng, Brent Hecht, and Haiyi Zhu. 2017. The Sharing Economy in Computing: A Systematic Literature Review. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 38 (Dec. 2017), 26 pages. https://doi.org/10.1145/3134673
[20]
Culture & Society Edinburgh Centre for Data and Edinburgh Futures Institute. 2022. Digital Worker Inquiry: Data, Solidarity, Leverage. https://digitalworkerinquiry.com/
[21]
Diana Farrell, Fiona Greig, and Amar Hamoudi. 2018. The online platform economy in 2018: Drivers, workers, sellers, and lessors. JPMorgan Chase Institute(2018).
[22]
George Ferguson and James F Allen. 1998. TRIPS: An integrated intelligent problem-solving assistant. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98). 567–572.
[23]
George Ferguson, James F Allen, and Bradford W Miller. 1996. TRAINS-95: Towards a Mixed-Initiative Planning Assistant. In Proceedings of The Third International Conference on artificial Intelligence Planning Systems (AIPS-96). 70–77.
[24]
Frances Flanagan and Michael Walker. 2021. How can unions use Artificial Intelligence to build power? The use of AI chatbots for labour organising in the US and Australia. New Technology, Work and Employment 36, 2 (2021), 159–176.
[25]
Yanbo Ge, Christopher R Knittel, Don MacKenzie, and Stephen Zoepf. 2020. Racial discrimination in transportation network companies. Journal of Public Economics 190 (2020), 104205.
[26]
California Attorney General. 2021. Proposition 22 Official Title and Summary. https://voterguide.sos.ca.gov/propositions/22/title-summary.htm
[27]
Mareike Glöss, Moira McGregor, and Barry Brown. 2016. Designing for Labour: Uber and the On-Demand Mobile Workforce. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). ACM, New York, NY, USA, 1632–1643. https://doi.org/10.1145/2858036.2858476
[28]
Mark Graham and Jamie Woodcock. 2018. Towards a fairer platform economy: introducing the Fairwork Foundation. Alternate Routes 29(2018), 242–253.
[29]
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), 100–103.
[30]
Mary L. Gray. 2019. Ghost work : how to stop Silicon Valley from building a new global underclass. Houghton Mifflin Harcourt, Boston.
[31]
Nick Greer, Jaime Teevan, and Shamsi T Iqbal. 2016. An introduction to technological support for writing. In ACM Conference on Human Factors in Computing Systems, Vol. 12.
[32]
Charles N Halaby. 2004. Panel models in sociological research: Theory into practice. Annu. Rev. Sociol. 30(2004), 507–544.
[33]
Anikó Hannák, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017. Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW ’17). ACM, New York, NY, USA, 1914–1933. https://doi.org/10.1145/2998181.2998327
[34]
Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019. Deconstructing Community-Based Collaborative Design: Towards More Equitable Participatory Design Engagements. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 216 (nov 2019), 25 pages. https://doi.org/10.1145/3359318
[35]
Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society 22, 7 (2019), 900–915.
[36]
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudik, and Hanna Wallach. 2019. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?ACM, New York, NY, USA, 1–16. https://doi.org/10.1145/3290605.3300830
[37]
Eric Horvitz. 1999. Principles of Mixed-Initiative User Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Pittsburgh, Pennsylvania, USA) (CHI ’99). ACM, New York, NY, USA, 159–166. https://doi.org/10.1145/302979.303030
[38]
Edwin L Hutchins, James D Hollan, and Donald A Norman. 1985. Direct manipulation interfaces. Human–computer interaction 1, 4 (1985), 311–338.
[39]
Emilie Jackson, Adam Looney, and Shanthi Ramnath. 2017. The rise of alternative work arrangements: Evidence and implications for tax filing and benefit coverage. Office of Tax Analysis Working Paper 114 (2017).
[40]
Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. 2015. A general suspiciousness metric for dense blocks in multimodal data. In 2015 IEEE International Conference on Data Mining. IEEE, 781–786.
[41]
Meng Jiang, Peng Cui, Fei Wang, Xinran Xu, Wenwu Zhu, and Shiqiang Yang. 2014. Fema: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1186–1195.
[42]
Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. 2016. Little is much: Bridging cross-platform behaviors through overlapped crowds. In Thirtieth AAAI Conference on Artificial Intelligence.
[43]
Isaac Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. In In Submission to CHI 2016.
[44]
Eliscia Kinder, Mohammad Hossein Jarrahi, and Will Sutherland. 2019. Gig Platforms, Tensions, Alliances and Ecosystems: An Actor-Network Perspective. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 212 (Nov. 2019), 26 pages. https://doi.org/10.1145/3359314
[45]
Toby Jia-Jun Li, Amos Azaria, and Brad A. Myers. 2017. SUGILITE: Creating Multimodal Smartphone Automation by Demonstration. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems(CHI ’17). ACM, New York, NY, USA, 6038–6049. https://doi.org/10.1145/3025453.3025483
[46]
Toby Jia-Jun Li, Jingya Chen, Haijun Xia, Tom M. Mitchell, and Brad A. Myers. 2020. Multi-Modal Repairs of Conversational Breakdowns in Task-Oriented Dialogs. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology(UIST 2020). ACM. https://doi.org/10.1145/3379337.3415820
[47]
Toby Jia-Jun Li, Igor Labutov, Xiaohan Nancy Li, Xiaoyi Zhang, Wenze Shi, Tom M. Mitchell, and Brad A. Myers. 2018. APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Verbal Instructions. In Proceedings of the 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2018).
[48]
Toby Jia-Jun Li, Tom Mitchell, and Brad Myers. 2020. Interactive Task Learning from GUI-Grounded Natural Language Instructions and Demonstrations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. ACL, Online, 215–223. https://doi.org/10.18653/v1/2020.acl-demos.25
[49]
Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, and Brad A. Myers. 2019. PUMICE: A Multi-Modal Agent that Learns Concepts and Conditionals from Natural Language and Demonstrations. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology(UIST 2019). ACM. https://doi.org/10.1145/3332165.3347899
[50]
Henry Lieberman and Hugo Liu. 2006. Feasibility studies for programming in natural language. In End user development. Springer, 459–473.
[51]
Henry Lieberman, Fabio Paternò, Markus Klann, and Volker Wulf. 2006. End-user development: An emerging paradigm. In End user development. Springer, 1–8.
[52]
Henry Lieberman, Fabio Paternò, Markus Klann, and Volker Wulf. 2006. End-User Development: An Emerging Paradigm. In End User Development. Springer, Dordrecht, 1–8. https://doi.org/10.1007/1-4020-5386-X_1
[53]
Christoph Lutz, Gemma Newlands, and Christian Fieseler. 2018. Emotional labor in the sharing economy. In Proceedings of the 51st Hawaii international conference on system sciences.
[54]
Ning F. Ma, Chien Wen Yuan, Moojan Ghafurian, and Benjamin V. Hanrahan. 2018. Using Stakeholder Theory to Examine Drivers’ Stake in Uber. ACM, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3173657
[55]
Michael David Maffie. 2020. The role of digital communities in organizing gig workers. Industrial Relations: A Journal of Economy and Society 59, 1(2020), 123–149.
[56]
Igor Mitic. 2021. 24+ Crucial Gig Economy Statistics and Facts | Fortunly.com. https://fortunly.com/statistics/gig-economy-statistics/
[57]
Marieke Möhlmann and Lior Zalmanson. 2017. Hands on the wheel: Navigating algorithmic management and Uber drivers’. In Autonomy’, in proceedings of the international conference on information systems (ICIS), Seoul South Korea. 10–13.
[58]
Gadi Nissim and Tomer Simon. 2021. The future of labor unions in the age of automation and at the dawn of AI. Technology in Society 67(2021), 101732.
[59]
US Bureau of Labor Statistics. 2017. Contingent and Alternative Employment Arrangements.
[60]
Kelly O’Halloran. 2022. How Arcade City has continued to grow, despite a few run-ins with the law | Built In Austin. https://www.builtinaustin.com/2017/12/06/arcade-city-where-are-they-now
[61]
Sharon Oviatt. 1999. Ten Myths of Multimodal Interaction. Commun. ACM 42, 11 (Nov. 1999), 74–81. https://doi.org/10.1145/319382.319398
[62]
Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, and Krishna Gummadi. 2020. Incremental fairness in two-sided market platforms: On smoothly updating recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 181–188.
[63]
Christopher A Pissarides. 2000. Equilibrium unemployment theory. MIT press.
[64]
Jeremias Prassl. 2018. Humans as a service: The promise and perils of work in the gig economy. Oxford University Press.
[65]
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). ACM, New York, NY, USA, 97–107. https://doi.org/10.1145/2818048.2820026
[66]
RideFair. 2022. RideFair - Rideshare platform for driver owned cooperatives. https://ridefair.io
[67]
Gig Worker Rising. 2021. Gig Workers Rising - News Coverage. https://gigworkersrising.org/get-informed/news/
[68]
Maria Ros, Shalom H Schwartz, and Shoshana Surkiss. 1999. Basic individual values, work values, and the meaning of work. Applied psychology 48, 1 (1999), 49–71.
[69]
Alex Rosenblat. 2018. Uberland: How Algorithms Are Rewriting the Rules of Work. University of California Press.
[70]
Alex Rosenblat, Karen EC Levy, Solon Barocas, and Tim Hwang. 2016. Discriminating tastes: Customer ratings as vehicles for bias. Data & Society (2016), 1–21.
[71]
Alex Rosenblat, Karen EC Levy, Solon Barocas, and Tim Hwang. 2017. Discriminating tastes: Uber’s customer ratings as vehicles for workplace discrimination. Policy & Internet 9, 3 (2017), 256–279.
[72]
Alex Rosenblat and Luke Stark. 2016. Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication 10 (2016), 27.
[73]
Ritam Sarmah, Yunpeng Ding, Di Wang, Cheuk Yin Phipson Lee, Toby Jia-Jun Li, and Xiang ’Anthony’ Chen. 2020. Geno: a Developer Tool for Authoring Multimodal Interaction on Existing Web Applications. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology(UIST 2020).
[74]
Trebor Scholz and Nathan Schneider. 2017. Ours to hack and to own: The rise of platform cooperativism, a new vision for the future of work and a fairer internet. OR books.
[75]
Shalom H Schwartz. 2012. An overview of the Schwartz theory of basic values. Online readings in Psychology and Culture 2, 1 (2012), 2307–0919.
[76]
Edward Segal. 2021. Labor Department Rescinds Trump Administration’s Policy On Gig Workers. https://www.forbes.com/sites/edwardsegal/2021/05/05/labor-department-rescinds-trump-administrations-policy-on-gig-workers/ Section: Leadership Strategy.
[77]
Tom Sühr, Asia J. Biega, Meike Zehlike, Krishna P. Gummadi, and Abhijnan Chakraborty. 2019. Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). ACM, New York, NY, USA, 3082–3092. https://doi.org/10.1145/3292500.3330793
[78]
Jacob Thebault-Spieker, Loren Terveen, and Brent Hecht. 2017. Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit. ACM Trans. Comput.-Hum. Interact. 24, 3, Article 21 (April 2017), 40 pages. https://doi.org/10.1145/3058499
[79]
Jacob Thebault-Spieker, Loren G. Terveen, and Brent Hecht. 2015. Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (Vancouver, BC, Canada) (CSCW ’15). ACM, New York, NY, USA, 265–275. https://doi.org/10.1145/2675133.2675278
[80]
Johanne R. Trippas, Damiano Spina, Falk Scholer, Ahmed Hassan Awadallah, Peter Bailey, Paul N. Bennett, Ryen W. White, Jonathan Liono, Yongli Ren, Flora D. Salim, and Mark Sanderson. 2019. Learning About Work Tasks to Inform Intelligent Assistant Design. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (Glasgow, Scotland UK) (CHIIR ’19). ACM, New York, NY, USA, 5–14. https://doi.org/10.1145/3295750.3298934
[81]
Stocksy United. 2022. Authentic Stock Photography. https://www.stocksy.com//, https://www.stocksy.com//
[82]
Steven Vallas and Juliet B Schor. 2020. What do platforms do? Understanding the gig economy. Annual Review of Sociology 46 (2020), 273–294.
[83]
Niels Van Doorn and Adam Badger. 2020. Platform capitalism’s hidden abode: producing data assets in the gig economy. Antipode 52, 5 (2020), 1475–1495.
[84]
Kurt Vandaele. 2018. Will trade unions survive in the platform economy? Emerging patterns of platform workers’ collective voice and representation in Europe. Emerging Patterns of Platform Workers’ Collective Voice and Representation in Europe (June 19, 2018). ETUI Research Paper-Working Paper (2018).
[85]
Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, and Nitesh V Chawla. 2020. Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2581–2589.
[86]
Mark Warschauer. 2004. Technology and social inclusion: Rethinking the digital divide. MIT press.
[87]
Alex C. Williams, Gloria Mark, Kristy Milland, Edward Lank, and Edith Law. 2019. The Perpetual Work Life of Crowdworkers: How Tooling Practices Increase Fragmentation in Crowdwork. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 24 (nov 2019), 28 pages. https://doi.org/10.1145/3359126
[88]
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.

Cited By

View all
  • (2024)Managing Gig Economy Workers Through Artificial IntelligenceFostering Industry-Academia Partnerships for Innovation-Driven Trade10.4018/979-8-3693-3096-8.ch002(17-30)Online publication date: 28-Jun-2024
  • (2023)Fast Drink: Mediating Empathy for Gig WorkersProceedings of the 2nd Empathy-Centric Design Workshop10.1145/3588967.3588975(1-6)Online publication date: 23-Apr-2023
  • (2023)Designing for AI-Powered Social Computing SystemsCompanion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3584931.3606951(572-575)Online publication date: 14-Oct-2023
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
CHIWORK '22: Proceedings of the 1st Annual Meeting of the Symposium on Human-Computer Interaction for Work
June 2022
215 pages
ISBN:9781450396554
DOI:10.1145/3533406
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2022

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. AI inequality
  2. gig workers
  3. human-AI collaboration
  4. intelligent assistants

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

CHIWORK 2022

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)149
  • Downloads (Last 6 weeks)28
Reflects downloads up to 14 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Managing Gig Economy Workers Through Artificial IntelligenceFostering Industry-Academia Partnerships for Innovation-Driven Trade10.4018/979-8-3693-3096-8.ch002(17-30)Online publication date: 28-Jun-2024
  • (2023)Fast Drink: Mediating Empathy for Gig WorkersProceedings of the 2nd Empathy-Centric Design Workshop10.1145/3588967.3588975(1-6)Online publication date: 23-Apr-2023
  • (2023)Designing for AI-Powered Social Computing SystemsCompanion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3584931.3606951(572-575)Online publication date: 14-Oct-2023
  • (2023)Research on WeChat Applet Intelligent Assistant based on Intelligent Recommendation Algorithm2023 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS58469.2023.10245918(1-5)Online publication date: 28-Jul-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media