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.
@article{arxiv.2103.16953,
title = {Mitigating Bias in Algorithmic Systems -- A Fish-Eye View},
author = {Kalia Orphanou and Jahna Otterbacher and Styliani Kleanthous and Khuyagbaatar Batsuren and Fausto Giunchiglia and Veronika Bogina and Avital Shulner Tal and AlanHartman and Tsvi Kuflik},
journal= {arXiv preprint arXiv:2103.16953},
year = {2022}
}