| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | February 25, 2020 |
| Latest Amendment Date: | June 23, 2022 |
| Award Number: | 1942259 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 1, 2020 |
| End Date: | May 31, 2022 (Estimated) |
| Total Intended Award Amount: | $485,359.00 |
| Total Awarded Amount to Date: | $56,839.00 |
| Funds Obligated to Date: |
FY 2021 = $0.00 FY 2022 = $0.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
201 OLD MAIN UNIVERSITY PARK PA US 16802-1503 (814)865-1372 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
110 Technology Center University Park PA US 16802-1503 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT 01002223DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project studies the ways that algorithmic management, using digital tools to automate and remotely manage workers, may negatively impact workers and their rights. Ride-hailing platforms, which are rapidly replacing traditional taxi services, are a canonical example of algorithmic management, where the software platform uses a variety of opaque means to automatically assign and evaluate work. The research community has had difficulty studying and improving these platforms, due to the platforms' proprietary and closed nature. This has exposed both drivers and passengers to biased or unfair outcomes, such as passengers poorly rating minority drivers or drivers declining rides from certain categories of customer. At the root of these problems are the opaque mechanisms for algorithmic management, and the several research and community efforts to make algorithmic management more palatable have largely impacted isolated functionality. These isolated cases point to a larger and urgent need to re-imagine these platforms as equitable workplaces, where we hold our algorithmic managers (and the people that develop them) to the same standard that we hold human managers. This work has the potential to extensively inform and redefine the standards and policies around how platforms that algorithmically manage work should be designed to form more equitable work environments.
To accomplish this goal it is necessary to build a more open platform, so that we can directly investigate these mechanisms. This project will develop an experimental ride-hailing platform that gives drivers and passengers control over parameters that impact algorithmic outcomes, as a means to understand and interact with the platform. It will serve as a testbed to conduct a series of mixed-methods studies that progressively increase in size and scope. These studies will be focused on three major themes: (1) understandable individual interactions with algorithmic managers, (2) equitable group interactions through algorithmic managers, and (3) self-governance for organizations with algorithmic management. In partnership with independent drivers and experts in labor relations, evaluation studies will use the platform to generate foundational knowledge about giving workers more control over the algorithms that manage them. Due to the open source nature of this work, any group that wants to begin their own cooperative platform will be able to utilize the application, tools, and resources developed in this research. These contributions will have a wide impact on how work is managed by algorithms and how to create human-centered algorithms and platforms for work.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
As Principal Investigator, Benjamin Hanrahan investigated new methods of algorithmic management aimed at informing and redefining the standards and policies around how platforms that algorithmically manage work should be designed to form more equitable workplaces. While this project was terminated early due to the Hanrahan's career change, this project funded a female minority graduate student for multiple years. This research supported multiple collaborations with industry and worker organizations for which several publications were produced, some of which are still under review at the end of the project.
This projected funded two primary collaborations. First, a collaboration with a worker-led group based in New York City. Where the topic of the collaboration was to understand how drivers needed to be supported and in what ways we could implement that support. The outcome of this project was a set of designs and supporting studies that was done in collaboration with the workers and the expectation is that they will implement these suggestions in their platform. These designs were based around a set of microservices provided to drivers to help them in identified areas of need, such as financing of tire purchasing. This project was part of two Masters theses, one of which was a male, minority student. A second collaboration was with industry, where the topic of the collaboration was studying how coop organizations were using their tools and what these organizations needed in terms of algorithmic support. The result of this study was a set of publications that have been submitted and a report for the coop organization.
Lastly, part of this project is the dissertation work for a female minority PhD student, where she investigated how to conduct human-centered algorithmic design. This involved bootstrapping algorithmic development with Bayesain belief networks that were based on qualitative interviews as a mock-up of the algorithm and gradually increasing the fidelity of models as more user interactions were captured.
Last Modified: 08/30/2022
Modified by: Benjamin V Hanrahan
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