# Policy-Gradient Training of Fair and Unbiased Ranking Functions

arXiv: https://arxiv.org/abs/1911.08054 · HTML: https://mlsanity.com/paper/1911.08054/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 16 of 16)

- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [End-to-end Learning for Fair Ranking Systems](https://mlsanity.com/paper/2111.10723/) (2021)
- [Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking](https://mlsanity.com/paper/2206.07247/) (2022)
- [Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://mlsanity.com/paper/2305.01522/) (2023)
- [Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank](https://mlsanity.com/paper/2407.19943/) (2024)
- [FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection](https://mlsanity.com/paper/2305.19407/) (2023)
- [Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking](https://mlsanity.com/paper/2204.13765/) (2022)
- [A Pre-processing Method for Fairness in Ranking](https://mlsanity.com/paper/2110.15503/) (2021)
- [Inference-time Stochastic Ranking with Risk Control](https://mlsanity.com/paper/2306.07188/) (2023)
- [Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity](https://mlsanity.com/paper/2511.06568/) (2025)
- [Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank](https://mlsanity.com/paper/2111.00735/) (2021)
- [Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling](https://mlsanity.com/paper/2205.06024/) (2022)
- [Recent Advances in the Foundations and Applications of Unbiased Learning to Rank](https://mlsanity.com/paper/2305.02914/) (2023)
- [Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness](https://mlsanity.com/paper/2308.13242/) (2023)
- [Learning to Re-rank with Constrained Meta-Optimal Transport](https://mlsanity.com/paper/2305.00319/) (2023)

## Abstract (arXiv)

While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and often lead to rich-get-richer dynamics. Moreover, even after the correction of such biases, reasons endogenous to the design of the learning algorithm can still lead to ranking policies that do not allocate exposure among items in a fair way. To address both exogenous and endogenous sources of unfairness, we present the first learning-to-rank approach that addresses both presentation bias and merit-based fairness of exposure simultaneously. Specifically, we define a class of amortized fairness-of-exposure constraints that can be chosen based on the needs of an application, and we show how these fairness criteria can be enforced despite the selection biases in implicit feedback data. The key result is an efficient and flexible policy-gradient algorithm, called FULTR, which is the first to enable the use of counterfactual estimators for both utility estimation and fairness constraints. Beyond the theoretical justification of the framework, we show empirically that the proposed algorithm can learn accurate and fair ranking policies from biased and noisy feedback.