Controlling Fairness and Bias in Dynamic Learning-to-Rank (2005.14713v1)

Abstract: Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.

• The paper proposes FairCo, a novel controller algorithm for dynamic learning-to-rank systems to actively manage fairness and mitigate bias.
• FairCo uses a proportional controller mechanism and an unbiased estimator with Inverse Propensity Scoring to correct feedback bias and ensure fair exposure distribution.
• Empirical evaluations on synthetic and real-world datasets demonstrate that FairCo effectively reduces unfairness while maintaining competitive ranking quality and utility.

Overview of Controlling Fairness and Bias in Dynamic Learning-to-Rank

The paper "Controlling Fairness and Bias in Dynamic Learning-to-Rank" by Morik et al. addresses the critical issue of fairness in learning-to-rank (LTR) systems, which are prevalent across various online platforms, such as search engines, recommendation systems, and news feeds. The authors identify two primary challenges: bias induced by ranking systems that can influence the collection of feedback and fair allocation of exposure to item groups in relation to their merit.

They propose a novel algorithm, FairCo, that aims to establish a balance between optimizing ranking performance and enforcing fairness constraints dynamically. This algorithm tackles the bias and unfairness in LTR, leveraging a controller-based approach where fairness criteria are enforced as a control problem.

Bias and Fairness in LTR

LTR systems, while useful, often encounter biases where highly ranked items garner more interaction and further feedback, thus perpetuating a rich-get-richer dynamics. Traditional LTR methodologies prioritize user utility, often disregarding item providers, leading to unfair exposure distributions. The algorithm in discussion confronts bias by implementing an unbiased estimator that corrects feedback bias using techniques like Inverse Propensity Scoring (IPS), pivotal for accurate relevance estimation.

In doing so, the paper extends the notion of merit-based fairness from historical contexts to a dynamic setting, formulating fairness as an amortized property over the learning process. The fairness model encompasses both exposure-based and impact-based considerations, striving for a fair representation of all item groups.

The FairCo Algorithm

At the core of the research is FairCo, a controller that integrates both relevance and fairness into the ranking strategy. It employs a proportional controller mechanism to dynamically adjust rankings based on fairness disparities. The controller actively mitigates the rich-get-richer effect and ensures a fair distribution of exposure. The algorithm adapatively updates relevance estimates using a newly designed unbiased estimator, which has been demonstrated to converge in real-world scenarios.

FairCo's effectiveness is validated against both synthetic and real-world datasets, revealing its ability to reduce unfairness while performing competitively in terms of utility. The proposed system operates efficiently without sacrificing either NDCG performance or fairness. Importantly, it does not mandate explicit exploration strategies, relying on user-driven feedback.

Empirical Evaluation

The empirical results are robust, showing FairCo outperforms naive LTR algorithms in managing exposure fairly across diverse scenarios. Using a news article aggregation scenario and real-world movie recommendation data, the paper illustrates that FairCo maintains a competitive balance between fairness and ranking quality. It achieves this without the computational overhead associated with alternative methods like linear programming baselines.

The realism in simulations reflects the dynamic nature of user interactions in online platforms, accounting for non-uniform user explorations and biases beyond simple rank position.

Implications and Future Directions

FairCo represents a significant contribution to managing fairness in dynamic systems by integrating theoretical underpinnings with practical relevance estimation methodologies. The algorithm's adaptability presents a viable solution for wide scale implementation in diverse LTR tasks.

Theoretically, the paper sets the stage for further exploration into fairness in dynamic settings beyond simple group-level fairness. In practice, it paves the way for deploying more equitable AI systems that balance the interests of all stakeholders.

Future research might delve into refining the control parameters and expanding the framework to accommodate even more complex fairness and merit relations. Furthermore, the application of FairCo and similar algorithms to emerging domains like personalized content delivery and societal decision systems promises intriguing possibilities.