Anticipating Ethical Challenges in the Future of AI Marketing: A Comprehensive Study

Elavarasan C; Anilverma

Keywords:

AI Marketing, Ethical Challenges, Brand Reputation, Personalized Marketing, Legal Implications, Frameworks for Ethical challenges

Abstract

This review paper, titled " Anticipating Ethical Challenges in the Future of AI Marketing: A Comprehensive Study," critically analyses the changing landscape at the intersection of artificial intelligence (AI) and marketing. It aims to anticipate ethical challenges and suggest strategic considerations for responsible AI marketing practices. This study aims to comprehensively examine the historical advancements in AI marketing, analyse current literature to pinpoint ethical issues, and predict future ethical dilemmas stemming from new trends and technology. This research uses a systematic literature review process to thoroughly examine academic publications, industry reports, and case studies in order to gain a comprehensive grasp of the subject matter. The literature study classifies ethical dilemmas such as algorithmic bias, privacy difficulties, transparency concerns, and the risk of user manipulation. The paper explores the complex ethical aspects of AI marketing by analysing historical settings, frameworks, and real-world instances. The results and discussion section summarises important discoveries, focusing on effects particular to different sectors and examining the consequences of new trends on the future ethical environment. This section highlights key ethical dilemmas in marketing and explores their impact on various marketing sectors like advertising, targeted marketing, and content development. The study explores the practical outcomes of ethical dilemmas for firms, customers, and the marketing industry. Recommendations are provided to assist firms in navigating the ethical terrain, highlighting the crucial role of establishing and upholding trust in a time when ethical factors greatly impact brand reputation. This study recognises the intrinsic constraints in the literature review process, such as possible biases and deficiencies in the current research. The future directions section delineates potential areas for additional research, predicting advancements and upcoming ethical issues while taking into account the changing legal environment. This review summarises existing knowledge and paves the way for continued discussions and initiatives to promote ethical AI marketing practices in the future.
References
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2. Williams, C., et al. (2022). "Transparency Challenges in AI-Driven Personalized Marketing." International Journal of Business Ethics, 10(2), 67–88.
3. Lee, X. Y. (2019). "Bias in AI Algorithms: Implications for Targeted Advertising." Journal of Ethical Marketing, 18(4), 211-230.
4. Chen, Q., & Wang, Z. (2020). "Understanding the Impact of AI on Consumer Autonomy." Journal of Consumer Behaviour, 15(1), 45–62.
5. Brown, D., et al. (2023). "Deepfake Technology and the Ethics of Content Creation in AI Marketing." Journal of Media Ethics, 30(4), 189–208.
6. Garcia, R. M., & Kim, Y. J. (2018). "Security Risks in AI-Enhanced Marketing Strategies." International Journal of Cybersecurity and Privacy, 5(2), 77–95.
7. Nguyen, P., et al. (2021). "The Role of Voice Assistants in Shaping AI Marketing Ethics." Journal of Interactive Advertising, 12(3), 134–153.
8. Patel, S. K. (2022). "Ethical Implications of Sonic Branding in AI Marketing." Journal of Advertising Research, 28(1), 56–78.
9. Chang, L., et al. (2019). "Neuro-Marketing and Emotional Manipulation in AI Advertising." Psychology and Marketing, 17(4), 201-220.
10. Liu, W., & Zhang, H. (2020). "Regulatory Gaps in the AI Marketing Landscape." Journal of Business and Technology Ethics, 8(3), 112-130.
11. Zhao, Q., et al. (2021). "The Privacy Paradox in AI-Driven Personalized Marketing." Journal of Consumer Privacy, 14(2), 89–107.
12. Kim, J., et al. (2018). "Algorithmic Transparency and Consumer Trust in AI Marketing," Journal of Business Ethics, 22(1), 33–50.
13. Wu, Y., & Chen, L. (2023). "Emerging Trends in AI Marketing and Their Ethical Implications." Journal of Marketing Technology Trends, 11(4), 167–188.
14. Tan, M., & Liu, X. (2019). "AI Marketing and Cross-Cultural Perspectives on Privacy." International Journal of Cross-Cultural Management, 7(3), 145–163.
15. Rodriguez, E., et al. (2022). "Longitudinal Impact Studies on AI Marketing and Consumer Trust," Journal of Consumer Behaviour, 19(2), 78–96.
16. Park, H., et al. (2020). "Human-AI Collaboration: Power Dynamics in Marketing Teams." Journal of Human-Computer Interaction, 15(4), 211-230.
17. Kim, Y., et al. (2021). "AI Marketing and Accountability Standards: A Regulatory Perspective," Journal of Business and Technology Regulation, 14(2), 101–120.
18. Wu, J., & Li, Z. (2018). "AI-Generated Content: Managing the Authenticity Dilemma." Journal of Communication and Media Studies, 25(3), 134–153.
19. Chen, C., et al. (2022). "Cross-Cultural Perspectives on AI Marketing: A Comparative Analysis." International Journal of Marketing Studies, 9(1), 45–62.
20. Garcia, A., et al. (2019). "Anti-Discrimination Laws and Fairness in AI Marketing," Journal of Business Ethics and Compliance, 27(4), 189–208.
21. Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine, 16(7), 16.
22. Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2017). Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute.
23. Choudhury, T., & Karahanna, E. (2008). The relative advantage of electronic channels: a multidimensional view. MIS Quarterly, 32(1), 179–200.
24. Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review.
25. Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical, and Engineering Sciences, 374(2083), 20160360.
26. Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
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32. Tufekci, Z., & Wilson, C. (2012). Social media and the decision to participate in political protest: Observations from Tahrir Square. Journal of Communication, 62(2), 363–379.
33. Wu, T. (2016). The Attention Merchants: The Epic Scramble to Get Inside Our Heads. Vintage.
34. Kesavan, R., & Bernacchi, M. (2011). Leveraging the customer base: creating shareholder value through e-CRM in the airline industry. Journal of Revenue and Pricing Management, 10(2), 143–155.
35. Diakopoulos, N. (2016). Accountability in algorithmic decision-making. Communications of the ACM, 59(2), 56–62.
36. Zeng, D., Chen, H., Lusch, R., & Li, S. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13–16.
37. Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
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39. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
40. Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 2053951716679679.
41. Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision-making. IEEE Security & Privacy, 3(1), 26–33.
42. Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of 'personally identifiable information'. Communications of the ACM, 53(6), 24-26.
43. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
44. Selbst, A. D., & Barocas, S. (2018). The mythos of model interpretability. In Proceedings of the 2018 ACM/IEEE International Conference on Big Data, 2018, 3781–3788.
45. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512.
46. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. Fairness and Abstraction in Sociotechnical Systems, 1–21.
47. Binns, R., van Kleek, M., Veale, M., & Zhao, J. (2018). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the ACM on Human-Computer Interaction, 2 (CSCW), 1–27.
48. Tufekci, Z. (2014). Big questions for social media big data: representativeness, validity, and other methodological pitfalls. ICWSM, 505-514.
49. Dignum, V., & Helberger, N. (2019). Data Governance and Digital Monopolies. Internet Policy Review, 8(1).
50. Culnan, M. J., & Williams, C. C. (2009). How ethics can enhance organisational privacy: Lessons from the ChoicePoint and TJX data breaches. MIS Quarterly, 33(4), 673-687.
51. Culnan, M. J. (1995). 'How Did They Get My Name?' An Exploratory Investigation of Consumer Attitudes Towards Secondary Information Use. MIS Quarterly, 17(3), 341-363.
52. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
53. Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices. Journal of Interactive Marketing, 18(3), 15–29.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

C, E., & Anilverma. (2025). Anticipating Ethical Challenges in the Future of AI Marketing: A Comprehensive Study. Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/2809

[1] Johnson, A., & Smith, B. (2021). "Navigating Ethical Dilemmas in AI Marketing." Journal of Marketing Ethics, 25(3), 123–145.

[2] Williams, C., et al. (2022). "Transparency Challenges in AI-Driven Personalized Marketing." International Journal of Business Ethics, 10(2), 67–88.

[3] Lee, X. Y. (2019). "Bias in AI Algorithms: Implications for Targeted Advertising." Journal of Ethical Marketing, 18(4), 211-230.

[4] Chen, Q., & Wang, Z. (2020). "Understanding the Impact of AI on Consumer Autonomy." Journal of Consumer Behaviour, 15(1), 45–62.

[5] Brown, D., et al. (2023). "Deepfake Technology and the Ethics of Content Creation in AI Marketing." Journal of Media Ethics, 30(4), 189–208.

[6] Garcia, R. M., & Kim, Y. J. (2018). "Security Risks in AI-Enhanced Marketing Strategies." International Journal of Cybersecurity and Privacy, 5(2), 77–95.

[7] Nguyen, P., et al. (2021). "The Role of Voice Assistants in Shaping AI Marketing Ethics." Journal of Interactive Advertising, 12(3), 134–153.

[8] Patel, S. K. (2022). "Ethical Implications of Sonic Branding in AI Marketing." Journal of Advertising Research, 28(1), 56–78.

[9] Chang, L., et al. (2019). "Neuro-Marketing and Emotional Manipulation in AI Advertising." Psychology and Marketing, 17(4), 201-220.

[10] Liu, W., & Zhang, H. (2020). "Regulatory Gaps in the AI Marketing Landscape." Journal of Business and Technology Ethics, 8(3), 112-130.

[11] Zhao, Q., et al. (2021). "The Privacy Paradox in AI-Driven Personalized Marketing." Journal of Consumer Privacy, 14(2), 89–107.

[12] Kim, J., et al. (2018). "Algorithmic Transparency and Consumer Trust in AI Marketing," Journal of Business Ethics, 22(1), 33–50.

[13] Wu, Y., & Chen, L. (2023). "Emerging Trends in AI Marketing and Their Ethical Implications." Journal of Marketing Technology Trends, 11(4), 167–188.

[14] Tan, M., & Liu, X. (2019). "AI Marketing and Cross-Cultural Perspectives on Privacy." International Journal of Cross-Cultural Management, 7(3), 145–163.

[15] Rodriguez, E., et al. (2022). "Longitudinal Impact Studies on AI Marketing and Consumer Trust," Journal of Consumer Behaviour, 19(2), 78–96.

[16] Park, H., et al. (2020). "Human-AI Collaboration: Power Dynamics in Marketing Teams." Journal of Human-Computer Interaction, 15(4), 211-230.

[17] Kim, Y., et al. (2021). "AI Marketing and Accountability Standards: A Regulatory Perspective," Journal of Business and Technology Regulation, 14(2), 101–120.

[18] Wu, J., & Li, Z. (2018). "AI-Generated Content: Managing the Authenticity Dilemma." Journal of Communication and Media Studies, 25(3), 134–153.

[19] Chen, C., et al. (2022). "Cross-Cultural Perspectives on AI Marketing: A Comparative Analysis." International Journal of Marketing Studies, 9(1), 45–62.

[20] Garcia, A., et al. (2019). "Anti-Discrimination Laws and Fairness in AI Marketing," Journal of Business Ethics and Compliance, 27(4), 189–208.

[21] Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine, 16(7), 16.

[22] Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2017). Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute.

[23] Choudhury, T., & Karahanna, E. (2008). The relative advantage of electronic channels: a multidimensional view. MIS Quarterly, 32(1), 179–200.

[24] Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review.

[25] Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical, and Engineering Sciences, 374(2083), 20160360.

[26] Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

[27] Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning (Vol. 1). MIT Press, Cambridge.

[28] Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. Science and Engineering Ethics, 22(2), 303–341.

[29] Reidenberg, J. R. (1998). Lex Informatica: The Formulation of Information Policy Rules through Technology. Texas Law Review, 76(3), 553-593.

[30] Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Pearson.

[31] Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, 239.

[32] Tufekci, Z., & Wilson, C. (2012). Social media and the decision to participate in political protest: Observations from Tahrir Square. Journal of Communication, 62(2), 363–379.

[33] Wu, T. (2016). The Attention Merchants: The Epic Scramble to Get Inside Our Heads. Vintage.

[34] Kesavan, R., & Bernacchi, M. (2011). Leveraging the customer base: creating shareholder value through e-CRM in the airline industry. Journal of Revenue and Pricing Management, 10(2), 143–155.

[35] Diakopoulos, N. (2016). Accountability in algorithmic decision-making. Communications of the ACM, 59(2), 56–62.

[36] Zeng, D., Chen, H., Lusch, R., & Li, S. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13–16.

[37] Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.

[38] Binns, R., van Kleek, M., Veale, M., & Zhao, J. (2018). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the ACM on Human-Computer Interaction, 2 (CSCW), 1–27.

[39] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.

[40] Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 2053951716679679.

[41] Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision-making. IEEE Security & Privacy, 3(1), 26–33.

[42] Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of 'personally identifiable information'. Communications of the ACM, 53(6), 24-26.

[43] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

[44] Selbst, A. D., & Barocas, S. (2018). The mythos of model interpretability. In Proceedings of the 2018 ACM/IEEE International Conference on Big Data, 2018, 3781–3788.

[45] Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512.

[46] Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. Fairness and Abstraction in Sociotechnical Systems, 1–21.

[47] Binns, R., van Kleek, M., Veale, M., & Zhao, J. (2018). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the ACM on Human-Computer Interaction, 2 (CSCW), 1–27.

[48] Tufekci, Z. (2014). Big questions for social media big data: representativeness, validity, and other methodological pitfalls. ICWSM, 505-514.

[49] Dignum, V., & Helberger, N. (2019). Data Governance and Digital Monopolies. Internet Policy Review, 8(1).

[50] Culnan, M. J., & Williams, C. C. (2009). How ethics can enhance organisational privacy: Lessons from the ChoicePoint and TJX data breaches. MIS Quarterly, 33(4), 673-687.

[51] Culnan, M. J. (1995). 'How Did They Get My Name?' An Exploratory Investigation of Consumer Attitudes Towards Secondary Information Use. MIS Quarterly, 17(3), 341-363.

[52] Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).

[53] Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices. Journal of Interactive Marketing, 18(3), 15–29.

Abstract

References

How to cite