Mitigating Bias in Algorithmic Systems -- A Fish-Eye View

• URL: http://arxiv.org/abs/2103.16953v2
• Date: Sun, 20 Feb 2022 19:59:35 GMT
• Title: Mitigating Bias in Algorithmic Systems -- A Fish-Eye View
• Authors: Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, AlanHartman and Tsvi Kuflik
• Abstract summary: 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.
• Score: 8.19357693559909
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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