Managing Bias in Machine Learning Projects

Item Type Conference or Workshop Item (Paper)

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.

Authors Fahse, Tobias; Huber, Viktoria & van Giffen, Benjamin
Research Team IWI4
Projects van Giffen, Dr. Benjamin; Brenner, Prof. Dr. Walter; Fahse, Tobias & Wulf, Dr. Jochen (2019) Management of Artificial Intelligence [applied research project] Official URL
Language English
Keywords Bias, Machine Learning, Project Management, Risk Management, Process Model
Subjects business studies
computer science
HSG Classification contribution to scientific community
HSG Profile Area SoM - Business Innovation
Refereed Yes
Date 9 March 2021
Event Title 16th International Conference on Wirtschaftsinformatik (WI)
Event Location Duisburg-Essen, Germany
Event Dates 09-11 Mar 2021
Contact Email Address
Depositing User Tobias Fahse
Date Deposited 24 Feb 2021 18:01
Last Modified 24 Feb 2021 18:10


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Fahse, Tobias; Huber, Viktoria & van Giffen, Benjamin: Managing Bias in Machine Learning Projects. 2021. - 16th International Conference on Wirtschaftsinformatik (WI). - Duisburg-Essen, Germany.

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