Mitigating Bias in Algorithmic Systems—A Fish-eye View

Abstract

Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders—including developers, end users, and third-parties—there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them, from a broad, cross-domain perspective. This survey provides a “fish-eye view,” examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment—bias detection, fairness management, and explainability management—and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context.