Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness

Fairness-aware machine learning: a perspective

Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of candidate CVs with job descriptions, algorithms may capture and propagate ethnici...

A comparative study of fairness-enhancing interventions in machine learning

Computers are increasingly used to make decisions that have significant impact in people’s lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following question...

Equitable Selection of Subjects: Equality Versus Fairness?

Fairness as a QoS Measure for Web Services

Service Oriented Architectures (SOAs) are component-based architectures, characterized by reusability, modularization and composition, usually offered by HTTP (web services) and often equipped with a Quality of Services (QoS) measure. In order to guarantee the fairness property to each client requesting a service, we propose a fair version of the (Soft) Concurrent Constraint language to deal wi...

Analysis of a queueing fairness measure

Scheduling policies significantly influence the performance of queueing systems, and performance measures like response and waiting times, throughput, or related properties have been comprehensively investigated in queueing and scheduling theory. The important issue of quantifying user perceived fairness has received less attention. Measuring fairness suffers from the subjective nature of fairn...