Measuring Fairness in Ranked Outputs (1610.08559v1)

Abstract: Ranking and scoring are ubiquitous. We consider the setting in which an institution, called a ranker, evaluates a set of individuals based on demographic, behavioral or other characteristics. The final output is a ranking that represents the relative quality of the individuals. While automatic and therefore seemingly objective, rankers can, and often do, discriminate against individuals and systematically disadvantage members of protected groups. This warrants a careful study of the fairness of a ranking scheme. In this paper we propose fairness measures for ranked outputs. We develop a data generation procedure that allows us to systematically control the degree of unfairness in the output, and study the behavior of our measures on these datasets. We then apply our proposed measures to several real datasets, and demonstrate cases of unfairness. Finally, we show preliminary results of incorporating our ranked fairness measures into an optimization framework, and show potential for improving fairness of ranked outputs while maintaining accuracy.

• The paper introduces novel fairness measures and an optimization framework to balance bias reduction and ranking accuracy.
• The methodology leverages statistical parity and logarithmic discounting using rND, rKL, and rRD to assess group representation.
• Empirical evaluations on ProPublica and German Credit datasets reveal key trade-offs between fairness and the maintenance of predictive accuracy.

An Analysis of Fairness in Ranked Outputs

"Measuring Fairness in Ranked Outputs" by Ke Yang and Julia Stoyanovich investigates the nuanced issues of fairness in algorithmically generated ranking systems. The paper addresses the potential for biases to arise in these systems, particularly biases that result in disadvantageous outcomes for members of protected groups. It presents novel fairness measures for ranked outputs and examines the impact of these measures through both synthetic and real dataset analyses. It also discusses the integration of these fairness measures into an optimization framework to improve the fairness of rankings without sacrificing accuracy.

The pervasive use of ranking algorithms across diverse domains, such as hiring, college admissions, and lending, underscores the importance of ensuring these systems operate fairly. The authors focus on statistical parity as a principal notion of fairness, specifically in contexts where a protected group can be identified by race, gender, or other immutable characteristics. Fairness in this context implies that the representation of a protected group in a ranking should reflect its proportion in the overall population.

Proposed Fairness Measures

The paper introduces several fairness measures including Normalized Discounted Difference (rND), Normalized Discounted KL-divergence (rKL), and Normalized Discounted Ratio (rRD). These measures quantify the difference in representation of the protected group at each rank compared to the broader population, weighted by their rank significance. The use of logarithmic discounting places greater emphasis on higher-ranking positions, which are typically more impactful in decision-making processes.

1. rND: A convex, continuous measure, offering an interpretable fairness score, though not differentiable at zero, limiting its functionality within optimization frameworks.
2. rKL: Utilizes KL-divergence, providing a smoother, differentiable alternative to rND, potentially offering better integration into optimization processes.
3. rRD: Asymmetric between protected and non-protected groups, rRD is most applicable when the protected group is a minority, thereby limiting its situational applicability.

These measures were sharply evaluated through synthetic datasets, demonstrating that fairness in rankings could be assessed effectively across different scenarios of group representation.

Empirical Evaluation

The empirical part includes application to real datasets—ProPublica's recidivism dataset and the German Credit dataset—each presenting unique biases. These datasets illustrate the disparities in fairness when different ranking criteria are applied. Notably, the ProPublica dataset showed higher rND scores compared to rKL, indicating greater disparity as measured by the former.

Optimization Approach

The authors developed an optimization framework, inspired by the fair representation methodology, to explore the balance between fairness and accuracy. The framework adjusts input representations to alleviate bias while maintaining critical information essential for accurate ranking. The paper confirms improvements in fairness metrics while cautioning that maintaining accuracy remains contingent on specific attribute weightings and dataset characteristics.

Implications and Future Directions

This research provides a structured method to evaluate and address fairness in ranking systems, delivering tools that could be pivotal for institutions relying on automated ranking to ensure equitable outcomes. The paper opens avenues for future work in refining these metrics and their computational efficiencies, as well as exploring multi-dimensional protected attributes beyond binary classifications. Developing robust methods to integrate fairness measures seamlessly into existing algorithmic frameworks could usher better fairness in AI applications across varied industries. Further exploration into algorithmic transparency and accountability could complement these technical advancements, ensuring inclusive and non-discriminatory uses of AI technology.