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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