Management of Artificial Intelligence
Type
applied research project
Start Date
01 January 2019
URI
Acronym
AIHSG
Status
ongoing
Keywords
Künstlicher Intelligenz
KI
Artificial Intelligence
AI
Management
IT-Management
Machine Learning
Deep Learning
Business Innovation
Design Thinking
Digital Innovation
Digital Venturing
Description
Das Research Lab für das Management von Künstlicher Intelligenz betreibt anwendungsorientierte Forschung und zielt auf den wertorientierten Einsatz von KI in Organisationen ab. Die Themenschwerpunkte des Labs werden sowohl in wissenschaftlichen Beiträgen, als auch in praxisorientierten Projekten bearbeitet, damit Organisationen Künstliche Intelligenz in Zukunft erfolgreich in produktiven IT-Anwendungen einsetzen können.
Leader contributor(s)
Member contributor(s)
Fahse, Tobias
Funder(s)
Range
HSG + other universities + partners
Range (De)
HSG + andere Unis + Partner
Division(s)
Eprints ID
247865

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Publication

Managing Bias in Machine Learning Projects

2021-03-09 , Fahse, Tobias , Huber, Viktoria , van Giffen, Benjamin

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.

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Publication

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

2022 , van Giffen, Benjamin , Herhausen, Dennis , Fahse, Tobias

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.

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Publication

Management von Künstlicher Intelligenz in Unternehmen

2020 , van Giffen, Benjamin , Borth, Damian , Brenner, Walter

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