Objectives: This research endeavors to explore the ethical ramifications when artificial intelligence is utilized in predictive marketing, especially regarding potential pitfalls like algorithmic bias, infringements on privacy, the deliberate manipulation of consumer actions, and the aggregation of market power, which may undermine fair business competition.
Methodology: A qualitative methodology was adopted for this study, involving In-Depth Interviews (IDIs) with a total of eight informants. The participants, who were data analysts and digital marketers representing various industry sectors, were recruited using both purposive and snowball sampling techniques. Data gathering spanned a six-week period and was subjected to thematic analysis to identify the prominent themes and issues.
Finding: Five main themes were found in this study: (1) the importance of fairness and non-discrimination in algorithms, (2) transparency in data governance, (3) protection of consumer privacy, (4) risk of behavioral manipulation, and (5) ethical implications of market share concentration by AI-based companies.
Conclusion: While AI can improve marketing efficiency, its use also risks creating social injustice and lowering consumer trust. Therefore, continuous data audits, transparent algorithm development, and strong consumer protection regulations are needed to ensure fair, ethical, and sustainable application of AI.

Anayat, S., & Rasool, G. (2022). Artificial intelligence marketing (AIM): connecting-the-dots using bibliometrics. Journal of Marketing Theory and Practice, 32(1), 114–135. https://doi.org/10.1080/10696679.2022.2103435

Andriyanty, R., Bachri, S., Rambe, D., & Majid, A. H. A. (2023). the Formal Sector Labor Performance Under the Shadow of Job Automation. Jurnal Aplikasi Manajemen, 21(3), 593–612. https://doi.org/10.21776/ub.jam.2023.021.03.04

Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management, 62(October 2021), 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433

Bag, S., Srivastava, G., Gupta, S., Sivarajah, U., & Wilmot, N. V. (2024). The effect of corporate ethical responsibility on social and environmental performance: An empirical study. Industrial Marketing Management, 117, 356-370. https://doi.org/10.1016/j.indmarman.2024.01.016

Barsha, S., & Munshi, S. A. (2024). Implementing artificial intelligence in library services: a review of current prospects and challenges of developing countries. Library Hi Tech News, 41(1), 7–10. https://doi.org/10.1108/LHTN-07-2023-0126

Belk, R. W., Belanche, D., & Flavián, C. (2023). Key concepts in artificial intelligence and technologies 4.0 in services. Service Business, 17(1), 1–9. https://doi.org/10.1007/s11628-023-00528-w

Bezuidenhout, C., Heffernan, T., Abbas, R., & Mehmet, M. (2023). The impact of Artificial Intelligence on the marketing practices of Professional Services Firms. Journal of Marketing Theory and Practice, 31(4), 516–537. https://doi.org/10.1080/10696679.2022.2090005

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330. https://doi.org/10.1016/j.ese.2023.100330

Blösser, M., & Weihrauch, A. (2024). A consumer perspective of AI certification – the current certification landscape, consumer approval and directions for future research. European Journal of Marketing, 58(2), 441–470. https://doi.org/10.1108/EJM-01-2023-0009

Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial Intelligence and the Modern Productivity Paradox. In The Economics of Artificial Intelligence (Issue May). https://doi.org/10.7208/chicago/9780226613475.003.0001

Cheng, Y., & Jiang, H. (2020). How Do AI-driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use. Journal of Broadcasting and Electronic Media, 64(4), 592–614. https://doi.org/10.1080/08838151.2020.1834296

Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428

Clarke, S., & Whittlestone, J. (2022). A survey of the potential long-Term impacts of ai: How ai could lead to long-Term changes in science, cooperation, power, epistemics and values. AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 192–202. https://doi.org/10.1145/3514094.3534131

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007

de Marcellis-Warin, N., Marty, F., Thelisson, E., & Warin, T. (2022). Artificial intelligence and consumer manipulations: from consumer’s counter algorithms to firm’s self-regulation tools. AI and Ethics, 2(2), 259–268. https://doi.org/10.1007/s43681-022-00149-5

Dwivedi, Y. K., & Wang, Y. (2022). Guest editorial: Artificial intelligence for B2B marketing: Challenges and opportunities. Industrial Marketing Management, 105, 109–113. https://doi.org/10.1016/j.indmarman.2022.06.001

Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.

Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), 403–413. https://doi.org/10.1016/j.ijinfomgt.2016.01.006

Ghanbarpour, T., Sahabeh, E., & Gustafsson, A. (2022). Consumer response to online behavioral advertising in a social media context: The role of perceived ad complicity. Psychology and Marketing, 39(10), 1853–1870. https://doi.org/10.1002/mar.21703

Hair, J. F., & Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. Journal of Marketing Theory and Practice, 29(1), 65–77. https://doi.org/10.1080/10696679.2020.1860683

Hari, H., Sharma, A., Verma, S., & Chaturvedi, R. (2025). Exploring ethical frontiers of artificial intelligence in marketing. Journal of Responsible Technology, 21, 100103. https://doi.org/10.1016/j.jrt.2024.100103

Hastuti, R., & Syafruddin. (2023). Ethical Considerations in the Age of Artificial Intelligence: Balancing Innovation and Social Values. West Science Social and Humanities Studies, 1(02), 76–87. https://doi.org/10.58812/wsshs.v1i02.191

Hollebeek, L. D., Sprott, D. E., & Brady, M. K. (2021). Rise of the Machines? Customer Engagement in Automated Service Interactions. Journal of Service Research, 24(1), 3–8. https://doi.org/10.1177/1094670520975110

Hunt, S. D., & Morgan, R. M. (1995). The Comparative Advantage Theory of Competition. Journal of Marketing, 59(2), 1. https://doi.org/10.2307/1252069

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2

Kim, A., & Briley, D. (2020). Finding the self in chance events. International Journal of Research in Marketing, 37(4), 853–867. https://doi.org/10.1016/j.ijresmar.2020.03.003

Kurniawanto, H. (2025). Implementation of integrated digital onboarding strategy in the Ministry of Transportation for organizational alignment and adaptation. MIX: Jurnal Ilmiah Manajemen, 15(2), Article 019. https://doi.org/10.22441/jurnal_mix.2025.v15i2.019

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data and Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679

Moews, B., Herrmann, J. M., & Ibikunle, G. (2019). Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Systems with Applications, 120, 197–206. https://doi.org/10.1016/j.eswa.2018.11.027

Patel, N., & Trivedi, S. (2020). Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty. Empirical Quests for Management Essences, 3(3), 1–24. https://www.researchberg.com/index.php/eqme/article/view/46

Quach, S., Thaichon, P., Martin, K. D., Weaven, S., & Palmatier, R. W. (2022). Digital technologies: tensions in privacy and data. Journal of the Academy of Marketing Science, 50(6), 1299–1323. https://doi.org/10.1007/s11747-022-00845-y

Rathore, D. B. (2023). Digital Transformation 4.0: Integration of Artificial Intelligence & Metaverse in Marketing. Eduzone : International Peer Reviewed/Refereed Academic Multidisciplinary Journal, 12(01), 42–48. https://doi.org/10.56614/eiprmj.v12i1y23.248

Sáez-Ortuño, L., Sanchez-Garcia, J., Forgas-Coll, S., Huertas-García, R., & Puertas-Prat, E. (2023). Impact of artificial intelligence on marketing research: Challenges and ethical considerations. Philosophy of Artificial Intelligence and Its Place in Society, August 2024, 18–42. https://doi.org/10.4018/978-1-6684-9591-9.ch002

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598

Shahzad, K., Khan, S. A., & Iqbal, A. (2024). Effects of artificial intelligence on university libraries: an SLR of cite score and IF journals’ articles from 2018 to 2023. Global Knowledge, Memory and Communication. https://doi.org/10.1108/GKMC-12-2023-0498

Shaik, M. (2023). Impact of artificial intelligence on marketing. East Asian Journal of Multidisciplinary Research, 2(3), 993–1004. https://doi.org/10.55927/eajmr.v2i3.3112

Strusani, D., & Houngbonon, G. V. (2019). The Role of Artificial Intelligence in Supporting Development in Emerging Markets, 1–8. https://doi.org/10.1596/32365

Umer Ghani, M., Awais, M., & Muzammul, M. (2019). Stock Market Prediction Using Machine Learning(ML)Algorithms. Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 97–116. https://doi.org/10.14201/ADCAIJ20198497116

van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144(February), 93–106. https://doi.org/10.1016/j.jbusres.2022.01.076

Varadarajan, R. (2020). Customer information resources advantage, marketing strategy and business performance: A market resources based view. Industrial Marketing Management, 89(April), 89–97. https://doi.org/10.1016/j.indmarman.2020.03.003