Apologies for cross-posting.
We are pleased to announce the Third Workshop on Computational Fair
Division (CFD),
https://sites.google.com/view/fairdivisionworkshop2025/call-for-contributions
co-located with the International Joint Conference on Artificial
Intelligence (IJCAI) https://2025.ijcai.org/ in Montreal, Canada from August 16-22, 2025.
CFD Website: https://sites.google.com/view/fairdivisionworkshop2025/
Submission link:
https://openreview.net/group?id=ijcai.org/IJCAI/2025/Workshop/CFD
Important Dates — All dates are 11:59 PM, Anywhere on Earth (AoE):
-
Submission Deadline: Apr 30, 2025
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Notification of Acceptance: May 30, 2025
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One-day workshop: August 16, 17, or 18, 2025
Papers should be submitted in IJCAI format, with a 7-page limit (excluding
references).
The cutting-edge field of fair division has seen explosive growth in recent
years, covering a wide range of important areas of interest. Recent
advancements have paved the way for ground-breaking research in this area,
answering important questions on how to allocate resources to agents with
competing preferences while ensuring fairness, efficiency, constraint
feasibility, and incentive compatibility. Moreover, exploring the power and
limitations of large language models, agentic AI, and other AI techniques
for tackling complex fair allocation decisions has gained a significant
amount of interest recently. This workshop brings together computational
fairness researchers from all walks of life---theoretical, empirical, and
applied---to discuss how to apply fair division to the challenges of modern
society. We invite submissions that push the boundaries of the
state-of-the-art in computational fair division on a variety of topics,
including:
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Classic fair allocation of indivisible items
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Resource allocation problems (e.g., cake cutting, house allocation,
matching, or apportionment)
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Constrained fair division
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Uncertainty & distortion in fair division
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Fair division in social networks
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Budget allocation
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Market design
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Competitive/market equilibria
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Combinatorial auctions or optimization with fairness consideration
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Incentives in fair division
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Automated theorem proving/SAT solving approaches for fair division
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Empirical analysis of resource allocation problems
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Datasets for and tools demonstrating practical implementation of fair
division algorithms
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ML approaches to fair division (e.g., learned preferences or online
procedures)
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Cooperative AI, Agentic AI, and LLM approaches to fair division
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Applications of fair division approaches to other algorithmic fairness
problems (e.g. ranking, fair LLMs, etc)
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Task allocation in multi-robotic systems
*Limited travel support is available from AIJ. More information will be
available after the notification of acceptance.
Thank you.
Best,
CFD 2025 organizers (contact.cfdworkshop@gmail.com)
Arpita Biswas, Hadi Hosseini, Joshua Kavner, Justin Payan, Sanjukta Roy,
Rohit Vaish, and Yair Zick
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