Co-Designing Organizational Justice Indicators for Algorithmic Systems

TL;DR

This paper proposes using organizational justice as a comprehensive framework for fairness in algorithmic systems, demonstrated through a case study with Kiva Microfunds to develop relevant metrics.

Contribution

It introduces a novel organizational justice framework for fairness, co-designed with stakeholders, to guide metric development and system deployment in real-world settings.

Findings

Identified normative concerns beyond distributional fairness.

Developed a set of organizational justice metrics for Kiva's recommender system.

Facilitated stakeholder discussions on fairness and system impact.

Abstract

Fairness in machine learning is often conceptualized narrowly in comparative, distributional terms. In studying stakeholders' concepts of fairness, we find that this framing is insufficient to capture the full range of issues raised. As an alternative, we propose organizational justice as a framework that subsumes distributional fairness as well as other normative concerns. We conduct a case study of organizational justice relative to personalized recommendation in the context of Kiva Microfunds, a nonprofit micro-lending organization whose mission is to increase financial access for underserved communities across the world. We report on the results of co-design workshops conducted with Kiva employees who are involved in different departments and whose roles often lead them to prioritize normative concerns that are most supportive of the stakeholders with whom they work most closely. We apply organizational justice to understand design trade-offs among different normative goals stakeholders invoke. Based on these goals, we identify a suite of metrics that Kiva employees can use to monitor and assess the recommender system's impact on their organizational justice concerns and to seed discussions within the organization about appropriate configuration and deployment of this system in context.