# Dynamic fairness-aware recommendation through multi-agent social choice

arXiv: https://arxiv.org/abs/2303.00968 · HTML: https://mlsanity.com/paper/2303.00968/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 84 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology](https://mlsanity.com/paper/1909.11869/) (2019)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Online Fair Division: analysing a Food Bank problem](https://mlsanity.com/paper/1502.07571/) (2015)
- [Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources](https://mlsanity.com/paper/1711.08345/) (2017)
- [Opportunistic Multi-aspect Fairness through Personalized Re-ranking](https://mlsanity.com/paper/2005.12974/) (2020)
- [Recommendation Fairness: From Static to Dynamic](https://mlsanity.com/paper/2109.03150/) (2021)
- [Long-term Dynamics of Fairness Intervention in Connection Recommender Systems](https://mlsanity.com/paper/2203.16432/) (2022)
- [Personalizing Fairness-aware Re-ranking](https://mlsanity.com/paper/1809.02921/) (2018)
- [FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings](https://mlsanity.com/paper/2205.00313/) (2022)
- ["And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation](https://mlsanity.com/paper/2009.02590/) (2020)
- [Who Pays? Personalization, Bossiness and the Cost of Fairness](https://mlsanity.com/paper/2209.04043/) (2022)

## Built on by (most cited first, 9 of 9)

- [A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models](https://mlsanity.com/paper/2503.23350/) (2025)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation](https://mlsanity.com/paper/2603.10673/) (2026)
- [Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF](https://mlsanity.com/paper/2309.08621/) (2023)
- [Social Choice for Heterogeneous Fairness in Recommendation](https://mlsanity.com/paper/2410.04551/) (2024)
- [Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems](https://mlsanity.com/paper/2605.02379/) (2026)
- [AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence](https://mlsanity.com/paper/2605.16291/) (2026)
- [Value-Preserving Architectures for Agentic AI Systems](https://mlsanity.com/paper/2609.03920/) (2026)

## Abstract (arXiv)

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 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. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. In particular, we express recommendation fairness as a novel 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. Simulations demonstrate the ability of the framework to integrate multiple fairness concerns in a dynamic way.