TY - JOUR
T1 - Algorithmic bias in machine learning-based marketing models
AU - Akter, Shahriar
AU - Dwivedi, Yogesh K.
AU - Sajib, Shahriar
AU - Biswas, Kumar
AU - Bandara, Ruwan J.
AU - Michael, Katina
N1 - Funding Information:
Dr Katina Michael is a professor at Arizona State University in the School for the Future of Innovation in Society and the School of Computing and Augmented Intelligence. She is the director of the Society Policy Engineering Collective and the founding program chair of the Master of Science in Public Interest Technology. Katina is the founding editor-in-chief of the IEEE Transactions on Technology and Society. She is a recipient of National Science Foundation grants related to adaptive AI training systems and user-centered design of smart cities, and has been previously awarded an Australian Research Council discovery grant in location-based services (www.katinamichael.com).
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/5
Y1 - 2022/5
N2 - This article introduces algorithmic bias in machine learning (ML) based marketing models. Although the dramatic growth of algorithmic decision making continues to gain momentum in marketing, research in this stream is still inadequate despite the devastating, asymmetric and oppressive impacts of algorithmic bias on various customer groups. To fill this void, this study presents a framework identifying the sources of algorithmic bias in marketing, drawing on the microfoundations of dynamic capability. Using a systematic literature review and in-depth interviews of ML professionals, the findings of the study show three primary dimensions (i.e., design bias, contextual bias and application bias) and ten corresponding subdimensions (model, data, method, cultural, social, personal, product, price, place and promotion). Synthesizing diverse perspectives using both theories and practices, we propose a framework to build a dynamic algorithm management capability to tackle algorithmic bias in ML-based marketing decision making.
AB - This article introduces algorithmic bias in machine learning (ML) based marketing models. Although the dramatic growth of algorithmic decision making continues to gain momentum in marketing, research in this stream is still inadequate despite the devastating, asymmetric and oppressive impacts of algorithmic bias on various customer groups. To fill this void, this study presents a framework identifying the sources of algorithmic bias in marketing, drawing on the microfoundations of dynamic capability. Using a systematic literature review and in-depth interviews of ML professionals, the findings of the study show three primary dimensions (i.e., design bias, contextual bias and application bias) and ten corresponding subdimensions (model, data, method, cultural, social, personal, product, price, place and promotion). Synthesizing diverse perspectives using both theories and practices, we propose a framework to build a dynamic algorithm management capability to tackle algorithmic bias in ML-based marketing decision making.
KW - Algorithmic bias
KW - Data bias
KW - Design bias
KW - Dynamic managerial capability
KW - Machine learning
KW - Marketing models
KW - Microfoundations
KW - Socio-cultural bias
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U2 - 10.1016/j.jbusres.2022.01.083
DO - 10.1016/j.jbusres.2022.01.083
M3 - Article
AN - SCOPUS:85124203468
SN - 0148-2963
VL - 144
SP - 201
EP - 216
JO - Journal of Business Research
JF - Journal of Business Research
ER -