Policy Learning for Fairness in Ranking (1902.04056v2)

Abstract: Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job placement, admissions). To address this need, we propose a general LTR framework that can optimize a wide range of utility metrics (e.g. NDCG) while satisfying fairness of exposure constraints with respect to the items. This framework expands the class of learnable ranking functions to stochastic ranking policies, which provides a language for rigorously expressing fairness specifications. Furthermore, we provide a new LTR algorithm called Fair-PG-Rank for directly searching the space of fair ranking policies via a policy-gradient approach. Beyond the theoretical evidence in deriving the framework and the algorithm, we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings.

• The paper presents a novel framework that integrates fairness constraints into ranking by employing stochastic policy-gradient methods.
• The Fair algorithm simultaneously optimizes IR utility and fairness metrics, outperforming traditional post-processing techniques.
• Empirical evaluations on synthetic and real-world datasets demonstrate a robust trade-off between user utility and reduced exposure bias.

Insights into "Policy Learning for Fairness in Ranking"

The paper "Policy Learning for Fairness in Ranking" by Ashudeep Singh and Thorsten Joachims introduces a novel Learning-to-Rank (LTR) framework with a focus on fairness in exposure allocation for ranked items. The research addresses the critical issue that traditional LTR methods optimize utility based on user-centric metrics, such as NDCG, while neglecting fair exposure distribution across the ranked items. This imbalance in focus can lead to real-world consequences, such as reinforcing existing biases in online marketplaces and search domains.

Overview of Contributions

The paper makes significant contributions both theoretically and empirically. It introduces a framework that broadens conventional ranking methods to include stochastic ranking policies. This shift from deterministic to stochastic methods allows the authors to formalize fairness constraints rigorously. They present a novel algorithm, Fair, which uses a policy-gradient approach to directly search for fair ranking policies that optimize user utility while adhering to fairness constraints.

Conceptual Framework: The authors propose a conceptual framework where fairness of exposure is explicitly incorporated into the LTR process. Instead of merely assessing utility, they introduce a mechanism to ensure that exposure is proportional to item merit which extends across both individual and group fairness paradigms.

Algorithmic Innovation: The paper details the Fair\ algorithm, a policy-gradient based method. This approach efficiently maximizes utility and fairness concurrently, by directly optimizing any chosen IR utility metric and a sophisticated class of fairness measures. This capability, rooted in stochastic policy expressions, represents an advancement over prior post-processing methods and heuristics, offering precise control over the fairness constraints.

Empirical Validation

Empirical results underline the competitive edge of the Fair\ algorithm over traditional LTR methods. Evaluation on both synthetic and real-world datasets, including the Yahoo! LTR challenge dataset and the German Credit dataset, illustrates its effectiveness in balancing the utility-fairness trade-off.

Utility and Fairness Trade-off: Results demonstrate that Fair\ efficiently reduces disparity in ranking while maintaining high user utility. Notably, the performance of Fair\ is superior to that of post-processing approaches which attempt to infuse fairness post hoc on biased estimated relevances.

Identification and Neutralization of Bias: A prominent highlight from the experiments is Fair’s capability to identify and mitigate biases arising from misleading features in datasets, thereby delivering more balanced exposure among groups without drastically sacrificing relevance utility.

Implications and Future Directions

The work presented by Singh and Joachims holds profound implications for the deployment of LTR systems in a fair and accountable manner. In practical terms, this fairness-aware LTR framework is applicable in diverse domains such as recruitment systems, online marketing, and information retrieval platforms, where exposure fairness is as crucial as user satisfaction.

Theoretically, the shift to stochastic ranking policies opens avenues for further exploration of using policy-gradient techniques to directly optimize complex fairness metrics alongside utility. Future work could delve into adapting these methods for real-time systems or extending them with differential privacy mechanisms to ensure fairness without compromising user data confidentiality.

Overall, the methodologies and insights from this paper provide a vital foundation for continued advancements in fair machine learning applications across various socio-technical systems.