AI and Ethics, and Marketing Education
Generative AI is moving faster than most marketing organisations and most marketing degrees. This week I'm looking at two sides of the same coin: what the latest peer-reviewed research says about doing AI ethically in marketing, and what it says about teaching AI responsibly to the marketers of tomorrow.
The Ethics of AI in Marketing
Promises, perils, and a framework for thinking about both
A recent paper from Kumar et al (2025) proposes a simple way to trace how generative AI's use inside a business eventually shows up as a benefit or a harm for society: action leads to capability, capability leads to transformation, and transformation leads to impact. Based on a literature review and interviews with 42 senior executives (a useful but small, qualitative sample, so treat the findings as illustrative rather than representative), the authors find real promise in areas like personalised financial guidance, accessibility tools, and adaptive learning alongside real risk in areas like over-reliance on AI for emotional support, a narrowing of independent problem-solving skills, and the formation of echo chambers. Their headline point for marketers: the same capability that creates value for a business can just as easily create harm for the people on the other end of it, and it's worth asking which outcome a given AI deployment is actually driving toward.
The ASSURANCE principles
Hermann and Puntoni (2025) take a more structured approach, distilling the AI ethics literature into eight principles that spell out ASSURANCE: Autonomy, Security, Sustainability, Representativeness, Accountability, Non-biasedness and non-discrimination, Crediting, and Empowerment. What stood out to me is their audit of existing AI governance frameworks (i.e. the EU AI Act, the OECD AI Principles, and others) against these eight principles. Not one framework covers all eight, and "Crediting" (who gets recognised or compensated when AI is trained on someone's original work) isn't addressed by any of them. For marketers using AI in content creation, that's a live gap, not a hypothetical one.
The AI Washing and AI Booing cycle
The third piece, from Ozturkcan and Bozdağ (2015), names a pattern many of us have probably sensed but not labelled: "AI Washing," where a company overstates what its AI can actually do (their comparison to greenwashing is apt), and "AI Booing," the public backlash that follows once the gap between the claim and the reality becomes visible. The authors argue these feed each other in a cycle. Overstatement erodes trust, backlash makes companies double down on symbolic reassurance rather than substance, and the cycle repeats. Their proposed way out rests on three pillars: transparency, ethical data management, and stakeholder collaboration, all anchored by genuine human oversight rather than automation for its own sake.
AI in the Marketing Classroom
Tutor, teammate, or tool?
Turning to education, Narang et al (2025) offer a helpful lens for how generative AI actually functions inside a marketing course. Drawing on role theory and the Community of Inquiry model, they argue GAI can show up as a tutor (personalised guidance on foundational concepts), a teammate (a collaborator in brainstorming and higher-order creative work), or a tool (executing tasks like data analysis so students can focus on interpretation). Based on a syllabus review and a survey of 14 instructors at one large US university (a small, single-institution sample worth treating as a starting point rather than a settled finding) they found that core, lower-level courses tended to lean on GAI as a tutor or tool, while higher-level electives got more value treating it as a teammate. It's a useful diagnostic for any of us designing a course: what role do you actually want the AI to play, and does your assignment design match that intent?
Learning AI ethics by doing it
Richter et al (2025) take a more hands-on approach in their case study of an undergraduate digital marketing course at the University of Auckland, where 197 students built an AI-assisted marketing campaign in partnership with a real consumer electronics company. Grounded in constructivist learning theory, the idea that students build knowledge through direct experience rather than passive instruction. The course introduced a purpose-built ethics framework called SACRAD (Specificity, Accuracy, Clarity, Relevance, Appropriateness, Depth) to guide students in evaluating AI-generated content responsibly, alongside tools like SEMrush, HubSpot, and AI image and video generators. The consistent theme across student and instructor feedback was that ethical reasoning about AI stuck better when it was practised on a real brief with real stakes, rather than taught as an abstract rule.
The common thread
Read together, these five papers point in the same direction: whether you're deploying AI in a campaign or in a classroom, the risk isn't the technology itself, It's treating AI as a replacement for judgement rather than a collaborator that still needs a human checking its work. The frameworks differ, but transparency, human oversight, and honest claims about what the AI is actually doing show up in every single one.
References
Hermann, E., & Puntoni, S. (2025). Generative AI in marketing and principles for ethical design and deployment. Journal of Public Policy & Marketing, 44(3), 332–349. https://doi.org/10.1177/07439156241309874
Kumar, V., Kotler, P., Gupta, S., & Rajan, B. (2025). Generative AI in marketing: Promises, perils, and public policy implications. Journal of Public Policy & Marketing, 44(3), 309–331. https://doi.org/10.1177/07439156241286499
Narang, U., Sachdev, V., & Liu, R. (2025). When AI wears many hats: The role of generative artificial intelligence in marketing education. Journal of Public Policy & Marketing. Advance online publication. https://doi.org/10.1177/07439156251328237
Ozturkcan, S., & Bozdağ, A. A. (2025). Responsible AI in marketing: AI booing and AI washing cycle of AI mistrust. International Journal of Market Research, 67(6), 696–722. https://doi.org/10.1177/14707853251379285
Richter, S., Giroux, M., Piven, I., Sima, H., & Dodd, P. (2025). A constructivist approach to integrating AI in marketing education: Bridging theory and practice. Journal of Marketing Education, 47(2), 94–111. https://doi.org/10.1177/02734753241288876
