Dennis Herhausen

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.

The Internet of Things (loT) promises to deliver enormous business value. More specifically, loT solutions disrupt existing business models by opening up novel service opportunities. In order to help companies understand the opportunities and challenges of this development, we shed light on different loT revenue models. Based on an inductive case study approach, we identify nine direct and indirect revenue patterns. The different types of revenue patterns all use loT-enabled services to create value for customers; the extent and the monetization of services, however, vary.