# Subgroup Fairness in Two-Sided Markets

arXiv: https://arxiv.org/abs/2106.02702 · HTML: https://mlsanity.com/paper/2106.02702/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 123 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 28 of 28)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [Robust Optimization for Fairness with Noisy Protected Groups](https://mlsanity.com/paper/2002.09343/) (2020)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [Average Individual Fairness: Algorithms, Generalization and Experiments](https://mlsanity.com/paper/1905.10607/) (2019)
- [Time-Varying Convex Optimization: Time-Structured Algorithms and Applications](https://mlsanity.com/paper/2006.08500/) (2020)
- [User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets](https://mlsanity.com/paper/2010.01470/) (2020)
- [Review of Mathematical frameworks for Fairness in Machine Learning](https://mlsanity.com/paper/2005.13755/) (2020)
- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours](https://mlsanity.com/paper/1912.08388/) (2019)
- [Beyond Individual and Group Fairness](https://mlsanity.com/paper/2008.09490/) (2020)
- [Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extension](https://mlsanity.com/paper/2003.03210/) (2020)
- [Fairness-Efficiency Tradeoffs in Dynamic Fair Division](https://mlsanity.com/paper/1907.11672/) (2019)
- [Dynamic Type Matching](https://mlsanity.com/paper/1811.07048/) (2018)
- [Edge-Weighted Online Bipartite Matching](https://mlsanity.com/paper/2005.01929/) (2020)
- [Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity](https://mlsanity.com/paper/1811.05100/) (2018)
- [Diverse Weighted Bipartite b-Matching](https://mlsanity.com/paper/1702.07134/) (2017)
- [Group-Fair Online Allocation in Continuous Time](https://mlsanity.com/paper/2006.06852/) (2020)
- [On the Ergodic Control of Ensembles](https://mlsanity.com/paper/1807.03256/) (2018)
- [CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization](https://mlsanity.com/paper/2005.02828/) (2020)
- [Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model](https://mlsanity.com/paper/1810.07903/) (2018)
- [How to Match when All Vertices Arrive Online](https://mlsanity.com/paper/1802.03905/) (2018)
- [Online Bipartite Matching with Amortized O(log2 n) Replacements](https://mlsanity.com/paper/1707.06063/) (2017)
- [Mixed-Integer Path-Stable Optimisation, with Applications in Model-Predictive Control of Water Systems](https://mlsanity.com/paper/2001.08121/) (2020)
- [Online Primal Dual Meets Online Matching with Stochastic Rewards: Configuration LP to the Rescue](https://mlsanity.com/paper/2002.01802/) (2020)

## Built on by (most cited first, 3 of 3)

- [Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual](https://mlsanity.com/paper/2201.06021/) (2022)
- [Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option](https://mlsanity.com/paper/2407.00032/) (2024)
- [Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks](https://mlsanity.com/paper/2507.04033/) (2025)

## Abstract (arXiv)

It is well known that two-sided markets are unfair in a number of ways. For instance, female workers at Uber earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalisation of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. While the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semi-definite programming. This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach, and trade-offs between Inter- and Intra-fairness.