Algorithmic bias in machine learning-based marketing models
Open accessAbstract
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.
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Algorithmic bias can distort audience selection and perpetuate exclusion of vulnerable groups that social marketing aims to benefit.
“El sesgo algorítmico puede distorsionar la selección de audiencias y perpetuar la exclusión de grupos vulnerables, precisamente aquellos que el marketing social busca beneficiar (Akter et al., 2022).”
From El Reto del Marketing Social ante la Inteligencia Artificial: un análisis comparado en distintos contextos geográficos y sistemas políticos (2026) · cites this paper as Akter et al., 2022
The comparative analysis found a common pattern across all four cases: a structural tension between AI's persuasive efficacy and the risk of violating target publics' autonomy and privacy.
“En primer lugar, todas las regiones enfrentan una tensión estructural entre la eficacia persuasiva que ofrece la IA y el riesgo de vulnerar la autonomía y la privacidad de los públicos objetivo, lo que confirma observaciones previas sobre el sesgo algorítmico en modelos de marketing (Akter et al., 2022).”
From El Reto del Marketing Social ante la Inteligencia Artificial: un análisis comparado en distintos contextos geográficos y sistemas políticos (2026) · cites this paper as Akter et al., 2022
Clicks, searches, GPS locations, and social media posts are captured often without users' clear consent or awareness and turned into probabilistic guesses about what we will do, want, or buy next.
“In simpler terms: the clicks, searches, GPS locations, and social media posts of everyday life are captured (often without users' clear consent or awareness) and turned into probabilistic guesses about what we will do, want, or buy next (Akter et al., 2022;Bashang & Puttanna, 2023;Gao et al., 2023).”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Akter et al., 2022
Managers should be vigilant about algorithmic biases when using AI.
“Moreover, the process optimization capability suggests that managers should be vigilant about algorithmic biases when using AI (Akter et al., 2022; Choudhury et al., 2020; van Giffen et al., 2022).”
From Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of <scp>AI</scp> applications (2025) · cites this paper as Akter et al., 2022
The Australian Tax Office prepopulates information on tax forms to simplify tax filing, automating taxpayer reporting and reducing involuntary errors.
“Similarly, the Australian Tax Office prepopulates information on the tax form to simplify tax filing (Akter et al., 2022), thereby automating taxpayer reporting and reducing involuntary errors.”
From Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of <scp>AI</scp> applications (2025) · cites this paper as Akter et al., 2022
AI adoption has potential unintended consequences across ethical, legal, and social aspects.
“the literature acknowledges the existence of potential unintended consequences of AI adoption across ethical, legal, and social aspects (ELSA; Akter et al., 2022).”
From Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of <scp>AI</scp> applications (2025) · cites this paper as Akter et al., 2022
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- Authors
- Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K.
- Affiliations
- University of Wollongong (AU)
- Symbiosis International University (IN)
- University of Technology Sydney (AU)
- University of Wollongong in Dubai (AE)
- Arizona State University (US)
- Corresponding author
- Dwivedi, Yogesh K.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 144
- Pages
- 201-216
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W4211255666
- Cited by
- 242
- References
- 26
- Citations by year
- 2026: 49 · 2025: 97 · 2024: 49 · 2023: 38 · 2022: 9
- FWCI
- 55.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Business Research (publishedVersion, cc-by-nc-nd)
- Research Online (University of Wollongong) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion)
- Funders
- National Science Foundation
- Australian Research Council
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- algorithmic bias, machine learning in marketing, algorithmic decision-making, dynamic capabilities, design bias, contextual bias, marketing decision-making, consumer discrimination
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