# Reducing Disparate Exposure in Ranking: A Learning To Rank Approach

arXiv: https://arxiv.org/abs/1805.08716 · HTML: https://mlsanity.com/paper/1805.08716/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 30 of 33)

- [Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers](https://mlsanity.com/paper/2104.13640/) (2021)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [End-to-end Learning for Fair Ranking Systems](https://mlsanity.com/paper/2111.10723/) (2021)
- [Fair Ranking with Noisy Protected Attributes](https://mlsanity.com/paper/2211.17067/) (2022)
- [Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers](https://mlsanity.com/paper/2404.03192/) (2024)
- [Optimizing generalized Gini indices for fairness in rankings](https://mlsanity.com/paper/2204.06521/) (2022)
- [Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings](https://mlsanity.com/paper/2202.03237/) (2022)
- [Fairness Through Regularization for Learning to Rank](https://mlsanity.com/paper/2102.05996/) (2021)
- [Fairness for Robust Learning to Rank](https://mlsanity.com/paper/2112.06288/) (2021)
- [10 Years of Fair Representations: Challenges and Opportunities](https://mlsanity.com/paper/2407.03834/) (2024)
- [Debiasing Neural Retrieval via In-batch Balancing Regularization](https://mlsanity.com/paper/2205.09240/) (2022)
- [Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval](https://mlsanity.com/paper/2503.21237/) (2025)
- [Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System](https://mlsanity.com/paper/2507.02000/) (2025)
- [Invariant Representations with Stochastically Quantized Neural Networks](https://mlsanity.com/paper/2208.02656/) (2022)
- [Fairness in Ranking under Disparate Uncertainty](https://mlsanity.com/paper/2309.01610/) (2023)
- [Measuring Bias in a Ranked List using Term-based Representations](https://mlsanity.com/paper/2403.05975/) (2024)
- [A Pre-processing Method for Fairness in Ranking](https://mlsanity.com/paper/2110.15503/) (2021)
- [Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems](https://mlsanity.com/paper/2406.17475/) (2024)
- [Contextual bandits with concave rewards, and an application to fair ranking](https://mlsanity.com/paper/2210.09957/) (2022)
- [Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations](https://mlsanity.com/paper/2501.04762/) (2025)
- [Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity](https://mlsanity.com/paper/2511.06568/) (2025)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages](https://mlsanity.com/paper/2402.05252/) (2024)
- [What's in a Query: Polarity-Aware Distribution-Based Fair Ranking](https://mlsanity.com/paper/2502.11429/) (2025)
- [Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval](https://mlsanity.com/paper/2508.18724/) (2025)
- [Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace](https://mlsanity.com/paper/2203.03718/) (2022)
- [Learning to Re-rank with Constrained Meta-Optimal Transport](https://mlsanity.com/paper/2305.00319/) (2023)
- [End-to-End Optimization and Learning of Fair Court Schedules](https://mlsanity.com/paper/2410.17415/) (2024)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)

## Abstract (arXiv)

Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational placement, and even social success of those being ranked. Researchers have become increasingly concerned with systematic biases in data-driven ranking models, and various post-processing methods have been proposed to mitigate discrimination and inequality of opportunity. This approach, however, has the disadvantage that it still allows an unfair ranking model to be trained. In this paper we explore a new in-processing approach: DELTR, a learning-to-rank framework that addresses potential issues of discrimination and unequal opportunity in rankings at training time. We measure these problems in terms of discrepancies in the average group exposure and design a ranker that optimizes search results in terms of relevance and in terms of reducing such discrepancies. We perform an extensive experimental study showing that being "colorblind" can be among the best or the worst choices from the perspective of relevance and exposure, depending on how much and which kind of bias is present in the training set. We show that our in-processing method performs better in terms of relevance and exposure than a pre-processing and a post-processing method across all tested scenarios.