Fairness of Exposure in Rankings (1802.07281v2)

Abstract: Rankings are ubiquitous in the online world today. As we have transitioned from finding books in libraries to ranking products, jobs, job applicants, opinions and potential romantic partners, there is a substantial precedent that ranking systems have a responsibility not only to their users but also to the items being ranked. To address these often conflicting responsibilities, we propose a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of exposure allocation. As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application specific, we show how a broad range of fairness constraints can be implemented using our framework, including forms of demographic parity, disparate treatment, and disparate impact constraints. We illustrate the effect of these constraints by providing empirical results on two ranking problems.

• The paper introduces a framework that integrates fairness constraints via probabilistic rankings and linear programming to optimize exposure allocation.
• The authors demonstrate, through empirical evaluation on job listings and news recommendations, that fair exposure adjustments can balance utility with equity.
• The approach provides a flexible, context-sensitive method for embedding fairness in ranking systems, paving the way for equitable algorithmic decision-making.

Fairness of Exposure in Rankings

The paper "Fairness of Exposure in Rankings" by Ashudeep Singh and Thorsten Joachims addresses a significant aspect of online systems where ranking mechanisms are pivotal. The authors propose a conceptual and computational framework aimed at incorporating fairness constraints in ranking systems, with a particular focus on the allocation of exposure.

Framework and Algorithms

At the core of the paper is a framework that formulates fairness constraints in rankings through exposure allocation. The authors designed this framework to maximize user utility while ensuring fairness for the items being ranked. The framework supports a wide range of fairness constraints, including demographic parity, disparate treatment, and disparate impact. Efficient algorithms are introduced to compute rankings that adhere to these fairness constraints while maximizing expected utility.

The proposed framework involves:

1. Probabilistic Rankings: By considering distributions over rankings, the paper leverages doubly stochastic matrices to model probabilistic rankings. This approach allows for efficient optimization, avoiding the combinatorial complexity of deterministic ranking.
2. Linear Programming: Fairness constraints are integrated as linear constraints within the ranking problem, facilitating the efficient solution of the problem via linear programming. This setup also permits the use of the Birkhoff-von Neumann decomposition to derive a convex combination of deterministic rankings from the probabilistic representation.
3. Utility and Fairness Balance: The objective function of the framework seeks to optimize expected utility, represented in a form general enough to encode various relevance-based utility measures. This optimization is balanced against fairness, handled via explicit fairness constraints.

Empirical Evaluation

The authors provide empirical results on example scenarios, such as job applicant rankings and news recommendation datasets. They demonstrate the practical application of the fairness constraints by showing how the algorithm adjusts rankings to reflect equitable exposure while maintaining a trade-off with utility loss. Specifically:

• Demographic Parity: Forces equal exposure across groups without consideration of relevance, which can lead to substantial utility loss.
• Disparate Treatment and Impact: These constraints tie exposure to relevance, aiming for proportional representation based on utility, resulting in a more nuanced and utility-aware fairness adjustment.

Theoretical and Practical Implications

The implication of this work is twofold. Theoretically, it provides a flexible mechanism for embedding fairness in ranking systems. Practically, it has potential applications in domains where equitable exposure is paramount, such as hiring platforms, content recommendation, and beyond.

While the paper refrains from claiming any universality regarding these fairness measures, it contributes significantly to the discourse on algorithmic fairness. The notion that the fairness of a ranking system depends on context is emphasized, with the framework designed to adapt to varying requirements across applications.

Future Directions

Future work could explore enhancing the framework to handle more complex utility measures and further refine the balance between fairness and efficiency. There is also a potential to extend this framework to accommodate real-time systems with dynamic user engagement and evolving relevance models.

In summary, "Fairness of Exposure in Rankings" delivers a robust framework and algorithms to incorporate and optimize fairness in ranking systems. This work marks a noteworthy advancement in addressing fairness in algorithmic decision-making, warranting further exploration and development within the AI and ML community.