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Managing Bias in Machine Learning Projects

In: Innovation Through Information Systems

Author

Listed:
  • Tobias Fahse

    (University of St. Gallen)

  • Viktoria Huber

    (University of St. Gallen)

  • Benjamin Giffen

    (University of St. Gallen)

Abstract

This paper introduces a framework for managing bias in machine learning (ML) projects. When ML-capabilities are used for decision making, they frequently affect the lives of many people. However, bias can lead to low model performance and misguided business decisions, resulting in fatal financial, social, and reputational impacts. This framework provides an overview of potential biases and corresponding mitigation methods for each phase of the well-established process model CRISP-DM. Eight distinct types of biases and 25 mitigation methods were identified through a literature review and allocated to six phases of the reference model in a synthesized way. Furthermore, some biases are mitigated in different phases as they occur. Our framework helps to create clarity in these multiple relationships, thus assisting project managers in avoiding biased ML-outcomes.

Suggested Citation

  • Tobias Fahse & Viktoria Huber & Benjamin Giffen, 2021. "Managing Bias in Machine Learning Projects," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 94-109, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_7
    DOI: 10.1007/978-3-030-86797-3_7
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