Managing Bias in Machine Learning Projects

Tobias Fahse (), Viktoria Huber () and Benjamin Giffen ()
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Tobias Fahse: University of St. Gallen
Viktoria Huber: University of St. Gallen
Benjamin Giffen: University of St. Gallen

A chapter in Innovation Through Information Systems, 2021, pp 94-109 from Springer

Abstract: 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.

Keywords: Bias; Machine learning; Project management; Risk management; Process model (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_7

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DOI: 10.1007/978-3-030-86797-3_7

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