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.
Original language | English |
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Article number | 87 |
Journal | ACM Computing Surveys |
Volume | 55 |
Issue number | 5 |
DOIs | |
State | Published - 31 May 2023 |
Bibliographical note
Publisher Copyright:© 2022 Association for Computing Machinery.
Keywords
- Algorithmic bias
- explainability
- fairness
- social bias
- transparency
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science