Mitigating Bias in Algorithmic Systems: A Fish-Eye View of Problems and Solutions Across Domains

03/31/2021 ∙ by Kalia Orphanou, et al. ∙ 0

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. 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 28

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.