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Fairness in recommender systems is a complex concept, involving multiple definitions, different parties for whom fairness is sought, and various scopes over which fairness might be measured. Re- searchers seeking fairness-aware systems have derived a variety of solutions, usually highly tailored to specific choices along each of these dimensions, and typically aimed at tackling a single fairness concern, i.e., a single definition for a specific stakeholder group and measurement scope. However, in practical contexts, there are a multiplicity of fairness concerns within a given recommendation application and solutions limited to a single dimension are therefore less useful. We explore a general solution to recommender system fairness using social choice methods to integrate multiple hetero- geneous definitions. In this paper, we extend group-fairness results from prior research to provider-side individual fairness, demon- strating in multiple datasets that both individual and group fairness objectives can be integrated and optimized jointly. We identify both synergies and tensions among different objectives with individ- ual fairness correlated with group fairness for some groups and anti-correlated with others.
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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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