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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Bibliographic Details

Published in:	Journal of business research Vol. 144; pp. 93 - 106
Main Authors:	van Giffen, Benjamin, Herhausen, Dennis, Fahse, Tobias
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.05.2022
Subjects:	Artificial intelligence Bias Case study Machine learning Mitigation methods Case study Mitigation methods Artificial intelligence Bias Machine learning
ISSN:	0148-2963
Online Access:	Get full text
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