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

Kalia Orphanou; Jahna Otterbacher; Styliani Kleanthous; Khuyagbaatar Batsuren; Fausto Giunchiglia; Veronika Bogina; Avital Shulner Tal; AlanHartman; Tsvi Kuflik

arXiv · 2103.16953

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

Kalia Orphanou·Jahna Otterbacher·Styliani Kleanthous·Khuyagbaatar Batsuren·Fausto Giunchiglia·Veronika Bogina·Avital Shulner Tal·AlanHartman·Tsvi Kuflik

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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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Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, AlanHartman, Tsvi Kuflik. 2021-03-31. Mitigating Bias in Algorithmic Systems -- A Fish-Eye View. https://arxiv.org/abs/2103.16953

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