Inaugural Workshop on Bridging Learning and Algorithmic Fairness in the Operation of Urban Infrastructure and Network Systems
Location: San Antonio, Texas, TX
Date: May 9, 2023
Organizers:
Alexandre Bayen, University of California, Berkeley
Devansh Jalota, Stanford University
Jessica Lazarus, University of California, Berkeley
Marco Pavone, Stanford University
Submission deadline: February 15, 2023 AoE
Please note that no further extensions will be given for the submission deadline. To facilitate the review process, we encourage you to submit your papers as early as possible.
Notification Date: March 8, 2023 AoE
Description:
In large-scale urban infrastructure systems, such as traffic networks and smart electric grids, there has been a growing interest in the design of intervention and control schemes to cope with efficiency losses that arise from the non-cooperative behavior of self-interested users. However, a vast majority of such control mechanisms (e.g., congestion pricing for road traffic mitigation) remain unimplemented as some users are made disproportionately worse off in the pursuit of system efficiency. Even worse, the efficacy of such control mechanisms relies on access to complete information on users' attributes and preferences, which may not be available in practice. Despite the lack of direct access to user preferences, the proliferation of user and system data unlocks significant opportunities and challenges to better inform the design of fair algorithmic decision-making systems for large-scale urban infrastructure applications. While the machine learning community has demonstrated the power of leveraging enormous amounts of data in reducing algorithm bias and making real-time decisions, the power of data-driven techniques to fairly operate urban infrastructure and networked systems is largely untapped. To address this gap, the goal of this workshop is three-fold:
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Emphasize data-driven machine learning techniques in the context of urban infrastructure and networked systems. The discussion of data-driven modeling, analysis, and control tools in applications such as road traffic management or the online optimization of electricity prices will help foster methodological developments to design fair algorithmic decision-making systems for urban infrastructure applications.
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Provide a platform to discuss the problems of and explore new research directions geared towards tapping the full potential of data in addressing equity challenges in urban infrastructure systems.
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Foster the development of novel cyber-physical systems (CPS) and internet of things (IoT) tools essential for advancing fair, data-driven, and intelligent urban infrastructure and networked systems.
-------Call for Papers-------
The Bridging Learning and Algorithmic Fairness in the Operation of Urban Infrastructure and Network Systems workshop at the 2023 CPS-IoT Week will take place on May 9, 2023 in San Antonio, Texas and is soliciting paper submissions (4 to 8 pages, including references) of novel work aimed at using algorithmic fairness and/or learning for urban infrastructure and networked systems. To this end, the workshop takes a broad perspective and welcomes work from various disciplines, including operations research, computer science, transportation, and the social sciences. Submissions should have theoretical or empirical results to demonstrate the approach and specify how the project fills a gap in the current literature.
Topics of interest include (but are not limited to):
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Data-driven methodologies for urban infrastructure systems
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Novel sensing-based methods to quantify fairness/equity measures in urban infrastructure applications
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Designing equitable smart transportation or power grid systems
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Redistributive mechanisms to improve access to opportunity, equity, or fairness for smart cities
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New fairness/equity definitions in urban infrastructure applications
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Trade-offs between fairness/equity and other objectives (e.g., efficiency, public acceptance) in urban infrastructure systems
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Evaluating fairness/equity issues in systems deployed for urban infrastructure applications
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Empirical studies of the above topics
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Pilot/Field deployments of CPS for equitable or data-driven operation of urban infrastructure systems
Submission deadline: February 15, 2023 AoE
Notification Date: March 8, 2023 AoE
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Devansh Jalota
Stanford University
Stanford CA
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