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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number87
JournalACM Computing Surveys
Volume55
Issue number5
DOIs
StatePublished - 3 Dec 2022

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2022 Association for Computing Machinery.

Keywords

  • Algorithmic bias
  • explainability
  • fairness
  • social bias
  • transparency

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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