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Session Chair: Monica Grace Maceli, Pratt Institute
Context-aware Coproduction: Implications for Recommendation Algorithms
J. Chen1, A. Doryab2, B. Hanrahan1, A. Yousfi3, J. Beck1, X. Wang1, V. Bellotti4, A. Dey5, J. Carroll1
1Information Sciences and Technology, Pennsylvania State University, University Park, Pennsylvania, United States; 2Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States; 3Hasso Plattner Institute, University of Potsdam, Potsdam, Germany; 4System Sciences Laboratory, PARC Inc., a Xerox Company, Palo Alto, California, United States; 5University of Washington, Seattle, Washington, United States
Coproduction is an important form of service exchange in local community where members perform and receive services among each other on non-profit basis. Local coproduction systems enhance community connections and re-energize neighborhoods but face difficulties matching relevant and convenient transaction opportunities. Context-aware recommendations can provide promising solutions, but are so far limited to matching spatio-temporal and static user contexts.
By analyzing data from a transportation-share app during a 3-week study with 23 participants, we extend the design scope for context-aware recommendation algorithms to include important community-based parameters such as sense of community. We find that inter- and intra-relationships between spatio-temporal and community-based social contexts significantly impact users' motivation to request or provide service. The results provide novel insights for designing context-aware recommendation algorithms for community coproduction services.
Algorithmic Management and Algorithmic Competencies: Understanding and Appropriating Algorithms in Gig work
M. H. Jarrahi1, W. Sutherland2
1University of North Carolina at Chapel Hill, United States of America; 2University of Washington, United States of America
Data-driven algorithms now enable digital labor platforms to automatically manage transactions between thousands of gig workers and service recipients. Recent research on algorithmic management outlines information asymmetries, which make it difficult for gig workers to gain control over their work due a lack of understanding how algorithms on digital labor plat-forms make important decisions such as assigning work and evaluating workers. By building on an empirical study of Upwork users, we make it clear that users are not passive recipients of algorithmic management. We explain how workers make sense of different automated features of the Up-work platform, developing a literacy for understanding and working with algorithms. We also highlight the ways through which workers may use this knowledge of algorithms to work around or manipulate them to retain some professional autonomy while working through the platform.
Agency Laundering and Algorithmic Decision Systems
A. Rubel1, A. Pham1, C. Castro2
1University of Wisconsin-Madison, United States of America; 2Florida International University, United States of America
This paper has two aims. The first is to explain a type of wrong that arises when agents obscure responsibility for their actions. Call it "agency laundering." The second is to use the concept of agency laundering to understand the underlying moral issues in a number of recent cases involving algorithmic decision systems.