A Ranking Approach to Fair Classification

TL;DR

This paper introduces a fair ranking-based decision system that leverages imperfect historical labels to improve fairness and accuracy in classification tasks, especially when true labels are unavailable.

Contribution

It proposes a novel distance-based ranking approach that incorporates historical decisions and mitigates bias, with theoretical guarantees of individual fairness.

Findings

Outperforms traditional classifiers in fairness and accuracy

Removes stereotypes from decision-making

Proven to be consistent with individual fairness principles

Abstract

Algorithmic decision systems are increasingly used in areas such as hiring, school admission, or loan approval. Typically, these systems rely on labeled data for training a classification model. However, in many scenarios, ground-truth labels are unavailable, and instead we have only access to imperfect labels as the result of (potentially biased) human-made decisions. Despite being imperfect, historical decisions often contain some useful information on the unobserved true labels. In this paper, we focus on scenarios where only imperfect labels are available and propose a new fair ranking-based decision system based on monotonic relationships between legitimate features and the outcome. Our approach is both intuitive and easy to implement, and thus particularly suitable for adoption in real-world settings. More in detail, we introduce a distance-based decision criterion, which incorporates useful information from historical decisions and accounts for unwanted correlation between protected and legitimate features. Through extensive experiments on synthetic and real-world data, we show that our method is fair in the sense that a) it assigns the desirable outcome to the most qualified individuals, and b) it removes the effect of stereotypes in decision-making, thereby outperforming traditional classification algorithms. Additionally, we are able to show theoretically that our method is consistent with a prominent concept of individual fairness which states that "similar individuals should be treated similarly."