Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner-Tal, Alan Hartman, Tsvi Kuflik
2021 Zenodo  
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
more » ... e 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.
doi:10.5281/zenodo.6240582 fatcat:vftoi4woebhrrp5tlmkclabgf4