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• W3147041282 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. 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." @default.
• W3147041282 created "2021-04-13" @default.
• W3147041282 creator A5001227032 @default.
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• W3147041282 creator A5082219837 @default.
• W3147041282 creator A5086221076 @default.
• W3147041282 date "2021-03-31" @default.
• W3147041282 modified "2023-09-27" @default.
• W3147041282 title "Mitigating Bias in Algorithmic Systems: A Fish-Eye View of Problems and Solutions Across Domains." @default.
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