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Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes (or some other metric) between a protected and unprotected group, and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. To address the fundamental problem of fairness in the presence of multiple stakeholders, with different definitions of fairness, we propose the Social Choice for Recommendation Under Fairness – Dynamic (SCRUF-D) architecture, which formalizes multistakeholder fairness in recommender systems as a two-stage social choice problem. In particular, we express recommendation fairness as a combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. We demonstrate the ability of our framework to dynamically incorporate multiple fairness concerns using both real-world and synthetic datasets.
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This content will become publicly available on May 1, 2026
Fairness Traps & Checks
Fairness traps represent opportunities to fortify fairness by identifying guiding principles, raising awareness about assumptions being made, and inserting fairness checks into the process.
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- Award ID(s):
- 2121930
- PAR ID:
- 10594174
- Publisher / Repository:
- Open Science Framework
- Date Published:
- Page Range / eLocation ID:
- DOI 10.17605/OSF.IO/AR8WG
- Subject(s) / Keyword(s):
- Compensation higher education faculty
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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