Source-linked AI summary
Multisided Fairness for Recommendation
Robin Burke
TL;DR
Fairness-aware recommendation must reconcile personalization with fair outcomes for multiple stakeholders in transactional platforms. The paper introduces a taxonomy covering consumers, providers, or both, proposes utility-based and dynamic architectural approaches, and identifies limits in provider-level guarantees and generalization.
Problem
Recommendation fairness cannot be addressed solely through user-centered personalization when transactions involve multiple stakeholder groups with potentially distinct fairness interests.
Method
The paper classifies systems by C-fairness, P-fairness, and CP-fairness and suggests utility-based, parity-based, bidding, and decoupled architectures.
Results
The paper shows that fairness in recommendation can be multisided and that provider-side individual fairness requires architectures sensitive to recommendation-environment dynamics.
Takeaways & Limitations
Fair recommendation design should account for stakeholder-specific outcomes rather than evaluating recommendation results only for one side of a transaction.
Takeaways & Limitations
The domain-specific utilities, business models, and interactions make experimentation difficult and limit generalization across recommendation scenarios.
Abstract
from arXiv · showhide
Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.
1 INTRODUCTION
Fairness in recommendation must account for personalization and the multiple stakeholders involved in transactions. The paper frames stakeholder utilities as a basis for designing and evaluating multistakeholder recommendation.
- 1.1 Personalization: Personalization means different users may prefer different items, preventing a shared global ranking from defining fairness.Employment recommendations may require controlling salary distributions for protected groups despite varied preferences about pay and job attributes.
- 1.2 Multistakeholder recommendation: Recommendation systems can facilitate transactions involving multiple parties, so fairness may need to address more than one participant group.Employment systems may need to consider both job seekers and employers, including recommendation rates for minority-owned businesses.
- 1.2 Multistakeholder recommendation: Multistakeholder recommendation considers parties beyond the end user, including consumers, providers, and the platform.Reciprocal recommendation addresses bilateral transactions in which recommendations must be acceptable to both parties.
- 1.3 Stakeholder utility: Formalizing stakeholder utilities provides a way to design and evaluate recommendation algorithms in multistakeholder settings.The system owner’s utility should be maximized while accounting for utilities of other stakeholders and domain-specific objectives.
2 MULTISIDED FAIRNESS
The paper classifies multisided fairness according to whether fairness concerns apply to consumers, providers, or both. It outlines candidate mechanisms while distinguishing list diversity from individual provider fairness and identifying open integration challenges.
- 2 MULTISIDED FAIRNESS: Multisided fairness arises when recommendation outcomes must satisfy fairness criteria on more than one side of a transaction.The taxonomy distinguishes consumer fairness, provider fairness, and fairness for both groups.
- C-fairness and P-fairness: C-fairness addresses disparate recommendation impact on protected consumer classes, while P-fairness addresses fairness for providers.C-fairness can involve fair credit offers, whereas P-fairness can target fair capital distribution or recommendation exposure for providers.
- 2.2 P-fairness: List diversity does not guarantee that any individual provider receives fair recommendation exposure or catalog coverage.The same diverse items could be recommended to everyone, leaving the broader provider set without fair outcomes.
- 2.2 P-fairness: Individual provider fairness requires dynamic handling of recommendation opportunities, such as provider budgets and bidding mechanisms.Recommendations may be delivered only when a provider successfully bids in a second-price auction for the opportunity.
- 2.2 P-fairness: Provider-side fairness can use purchasing parity, allocating B/2p budget to each protected provider and B/2q to each other provider.Here, p and q denote the numbers of protected and other providers, and B is the total advertising budget.
- 2.3 CP-fairness: CP-fairness requires fairness for both consumers and providers, including protected renters, landlords, job seekers, and employers.The proposed consumer- and provider-side solutions are decoupled, leaving their combined effects on stakeholder outcomes and system performance for future research.
3 CONCLUSION
The paper outlines multisided fairness approaches for personalized recommendation, emphasizing provider-side fairness and architectures sensitive to recommendation dynamics. It also identifies domain-specific utilities, scarce experimental datasets, and limited generalizability as central challenges.
- Multisided fairness extends classification fairness to personalized recommendation, where regulatory outcomes may conflict with user preferences.
- Provider-side fairness, particularly at the individual level, requires architectures that account for the dynamics of the recommendation environment.
- Recommendation stakeholders’ utilities depend on the recommended item, platform business model, and enabled interactions.
- Domain-specific environments make suitable experimental datasets difficult to find and generalization across recommendation scenarios challenging.