On Adaptive Fairness in Software Systems

TL;DR

This paper introduces a method for maintaining fairness in software systems amid changing requirements and environments by combining requirements-driven and resource-driven adaptation, demonstrated through a shopping experience example.

Contribution

It proposes a novel approach to adaptive fairness that dynamically adjusts to evolving fairness requirements and resources in software systems.

Findings

Models for fairness requirements and resources enable runtime adaptation.

The approach supports stakeholders in maintaining fairness in dynamic environments.

Demonstrated effectiveness through a shopping experience case study.

Abstract

Software systems are increasingly making decisions on behalf of humans, raising concerns about the fairness of such decisions. Such concerns are usually attributed to flaws in algorithmic design or biased data, but we argue that they are often the result of a lack of explicit specification of fairness requirements. However, such requirements are challenging to elicit, a problem exacerbated by increasingly dynamic environments in which software systems operate, as well as stakeholders' changing needs. Therefore, capturing all fairness requirements during the production of software is challenging, and is insufficient for addressing software changes post deployment. In this paper, we propose adaptive fairness as a means for maintaining the satisfaction of changing fairness requirements. We demonstrate how to combine requirements-driven and resource-driven adaptation in order to address variabilities in both fairness requirements and their associated resources. Using models for fairness requirements, resources, and their relations, we show how the approach can be used to provide systems owners and end-users with capabilities that reflect adaptive fairness behaviours at runtime. We demonstrate our approach using an example drawn from shopping experiences of citizens. We conclude with a discussion of open research challenges in the engineering of adaptive fairness in human-facing software systems.