Agency Flow in a Multi-user Ridesharing System
Open accessAbstract
In modern versions of sociotechnical environments, people's actions are moderated by algorithms. This usually leads to prioritizing a particular group of people's experience over the other. If AI-infused systems are to be introduced to these environments, success requires that people (regardless of roles and capacities) feel in control. We performed a preliminary exploratory study with 28 participants on Prolific with ridesharing experience using some ridesharing environment descriptions from the views of different user roles at varying agency levels. We found that prioritizing one group over the other can lead to a net negative experience for all the user groups. Enabling agency for all user groups was more likely to lead to positive characteristics like negotiation and collaboration than the logical expectations of conflict.
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AI's growing capabilities drive human-AI partnerships, bringing agency, control, and autonomy into the CSCW community's focus.
“Here, AI's growing capabilities drive human-AI partnerships, bringing agency, control, and autonomy into the CSCW community's focus [2, 34, 99].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [2]
The Introduction and Related Works (page not provided) discusses integrating people with AI-infused systems and identifies agency and autonomy as concerns; the Abstract also emphasizes users feeling in control. Our copy does not say that AI’s growing capabilities drive human–AI partnerships or establish that these issues have become a focus of the CSCW community. (LLM, conf=0.79)
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Cites
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Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management
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Conceptualizing the Relationship between AI Explanations and User AgencyPreprint
Adenuga, I. J., & Dodge, J. E. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.03193
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Research works printed but not linked: 4
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Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36 · see citing works
- Authors
- Adenuga, I. J., & Dodge, J. E.
- Affiliations
- Pennsylvania State University (US)
- Venue
- Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing
- Publisher
- ACM
- Published
- 2024-11-11
- Pages
- 336-341
- OpenAlex
- W4404331646
- Semantic Scholar
- df0e58307aa15328fcbe7a2c9727b8a21ca194f5
- Cited by
- 0
- References
- 12
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion)
- Funders
- National Science Foundation (1942259)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- ride-sharing, user agency, multi-user systems, algorithmic moderation, socio-technical systems, user roles, perceived fairness, negotiation, collaboration, conflict, algorithmic control
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