Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods

Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance...

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Veröffentlicht in:Journal of business research Jg. 144; S. 93 - 106
Hauptverfasser: van Giffen, Benjamin, Herhausen, Dennis, Fahse, Tobias
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.05.2022
Schlagworte:
Artificial intelligence
Bias
Case study
Machine learning
Mitigation methods
Case study
Mitigation methods
Artificial intelligence
Bias
Machine learning
ISSN:0148-2963
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Zusammenfassung:Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.
ISSN:0148-2963
DOI:10.1016/j.jbusres.2022.01.076