87 Mitigating Bias in Algorithmic Systems—A Fish-eye View KALIA ORPHANOU, Open University of Cyprus JAHNA OTTERBACHER and STYLIANI KLEANTHOUS, Open University of Cyprus & CYENS Centre of Excellence KHUYAGBAATAR BATSUREN, National University of Mongolia FAUSTO GIUNCHIGLIA, The University of Trento VERONIKA BOGINA, AVITAL SHULNER TAL, ALAN HARTMAN, and TSVI KUFLIK, The University of Haifa 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 per- spective. This survey provides a “fsh-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. CCS Concepts: • Information systems → Decision support systems;• Social and professional topics; Additional Key Words and Phrases: Algorithmic bias, explainability, fairness, social bias, transparency ACM Reference format: Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, Alan Hartman, and Tsvi Kufik. 2022. Mitigating Bias in Algorithmic Systems—A Fish-eye View. ACM Comput. Surv. 55, 5, Article 87 (December 2022), 37 pages. https://doi.org/10.1145/3527152 1 INTRODUCTION Long before the widespread use of algorithmic systems driven by big data, Friedman and Nissenbaum [68], writing in the ACM TOIS in 1996, argued that “freedom from bias” should be This project is partially funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 810105 (CyCAT). Otterbacher and Kleanthous are also supported by the Cyprus Research and Innovation Foundation under grant EXCELLENCE/0918/0086 (DESCANT) and by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 739578 (RISE). Authors’ addresses: K. Orphanou, Open University of Cyprus, Cyprus; email: kalia.orphanou@ouc.ac.cy; J. Otterbacher and S. Kleanthous, Open University of Cyprus & CYENS Centre of Excellence, Cyprus; email: jahna.otterbacher@ouc.ac.cy; K. Batsuren, National University of Mongolia, Mongolia; email: khuyagbaatar@num.edu.mn; F. Giunchiglia, The University of Trento, Italy; email: fausto@disi.unitn.it; V. Bogina, A. S. Tal, A. Hartman, and T. Kufik, The University of Haifa, Israel; emails: sveron@gmail.com, alan.hartman.gm@gmail.com, tsvikak@is.haifa.ac.il. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 0360-0300/2022/12-ART87 $15.00 https://doi.org/10.1145/3527152 ACM Computing Surveys, Vol. 55, No. 5, Article 87. Publication date: December 2022.