Co-Designing Organizational Justice Indicators for Algorithmic Systems

2026-05-12

AI Summary

This study addresses the limitations of prevailing machine learning fairness frameworks, which predominantly emphasize distributive fairness and struggle to accommodate the diverse justice concerns of multiple stakeholders in algorithmic systems. To bridge this gap, the authors introduce organizational justice theory into algorithm design for the first time. Through co-design workshops with Kiva staff, complemented by qualitative interviews and theoretical analysis, they identify normative concerns about personalized recommendation systems across different organizational units. Building on these insights, they develop an actionable and interpretable set of organizational justice evaluation metrics. This framework not only facilitates trade-offs among multidimensional justice objectives and informs system configuration but also fosters cross-departmental normative dialogue regarding algorithmic deployment. The proposed metrics have been successfully implemented in Kiva’s microlending platform.

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.

Problem

• algorithmic fairness
• organizational justice
• stakeholder concerns
• recommender systems
• normative goals

Innovation

• organizational justice
• algorithmic fairness
• co-design
• recommender systems
• normative metrics