Generative artificial intelligence: ethical considerations
In today society, technology expands rapidly and especially artificial intelligence. So, it is important to be aware of the ethical issues associated with generative AI in marketing. As a matter of fact, generative AI has become a key player in everyday life. For example, more than 70% of companies relies on generative AI to carry out at least one business function (Berry, 2025). Moreover, marketers mainly use it to develop campaigns, personalize content, test creative assets and drive strategy (Berry, 2025). Most of the decision that were made by human such as creative work are now handled by automated systems.
Despite these major advancements, it is essential to consider the ethical questions. According to Gao et al. (2023), the industry stands at a crossroads between new opportunities and pressing ethical issues. As we know, technology evolves much faster than the rules and laws designed to govern it and regulations take time to be put in place. In addition, companies face major risks regarding copyright, inadequate policies, inaccurate outputs and illegal audience segmentation (Berry, 2025). When it comes to AI, responsibility is usually unclear, and those who should be held accountable have not been clearly defined. For example, a McKinsey survey of generative AI users suggests that a large share of them do not systematically review AI-generated content (Berry, 2025). It is important to keep in mind that marketing greatly influences how consumers perceive brands and how they make decisions. Because of this, these gaps are not just technical but also raise ethical concerns about trust, fairness and accountability. This article examines the ethical risks of generative AI in advertising and argues that they must be addressed now, before the technology becomes standard practice.
Generative Artificial Intelligence
In a few words, generative AI is an intelligent systems capable to learn from a large dataset and being able to produce new content, such as text, images, videos, or music that can look like human creation (Huh & Malthouse, 2025).
A deepfake is a digital imitation that is very convincing about a person’s image, voice, or likeness and also created without the person’s consent. In addition, deepfakes can undermine a brand message and identity, and once released, they are very difficult to trace. (Berry, 2025).
An algorithmic bias happens when the AI system produces incorrect or discriminatory responses and these kinds of answers are caused by biased data or the prejudices of the AI’s creators. Naturally, AI tools are built by human being and inherit the biases of their creators (Berry, 2025). So, it is important that their outputs must be checked by humans (Berry, 2025). Moreover, biased training datasets lead AI-generated advertisements to reinforce racial and gender stereotypes (Sharma & Lal, 2024).
Risks of using AI in marketing
There are three main challenges in using AI in marketing such as ethical (biases), moral (environmental damage, workforce reduction, and lack of accountability), and legal (copyright and deepfakes) (Berry, 2025). Therefore, there are three risks that emerge from advertising studies such as algorithmic bias, consumer deception that erodes trust, and a lack of clarity regarding ownership and responsibility. Below, we examine each point using a concrete case, its real impact, and a potential solution.
Ethical considerations in the use of AI-generated images
Risk 1: Algorithmic bias and stereotypes
For the first risk, the example is based on an artificial intelligence system created by OpenAI, in the MIT Technology Review indicate that DALL-E 2, associated white men with the term “CEO” or “director” approximatively 97% of the time (Berry, 2025). Moreover, interviews with advertising designers show that AI-generated images reinforce stereotypes present in their training data (Sharma & Lal, 2024).
As a result, the problem of biased visuals is harmful for the society and business. In today’s multicultural society, erasing minorities only continues to grow toxic stereotypes, while damaging the brand image of advertisers who fail to reflect the diversity of their customer base.
Ultimately, the solution will be to diversify training data of the AI. In other words, the set of information provided to an AI system requires diversification so it can learn to perform a task, and to audit these datasets regularly (Sharma & Lal, 2024).
The ethical risk of AI: a threat to creativity
Risk 2: Deception and loss of consumer trust
For the second risk, the example is based on the fact that with artificial intelligence, companies can now create realistic visuals such as virtual human stand-ins or deepfakes, without admitting that the content is generated by artificial intelligence (Huh & Malthouse, 2025).
As a result, the problem when companies choose not to inform consumers that they are be using AI-generated content in their campaigns, it becomes difficult to distinguish what is really created by the AI and what is created by human being. So, this lack of transparency is likely to mislead consumers, provoke strong discontent, and profoundly affect the credibility of the brand and its advertising messages (Huh & Malthouse, 2025).
Ultimately, it is important that marketing professionals place more emphasis on transparency in order to better protect consumers and to adopt ethical practices, such as disclosures stating this content is AI-generated. So, by implementing rigorous standards, marketers can prevent misleading representations and make it possible to distinguish between human creation and AI creation (Huh & Malthouse, 2025).
Ethics and AI: the risk that nobody can ignore
Risk 3: Copyright, ownership and accountability gaps
For the third risk, the example is based on a statement made by the U.S. Copyright Office, where they said that content generated by AI is not under copyright protection and therefore belongs to no individual or company. In other words, anyone is free to reuse contents without legal recourse. Also, AI can imitate the style of real artists based on the illustrations or photographs used to train it, often with few legal consequences or restrictions (Berry, 2025).
As a result, the major problem is that brands and companies cannot protect what they do not own. Therefore, when they create AI-generated content, it is not legally protected, which means anyone can reuse it as they please. Moreover, the worst is that original creators risk going uncredited and uncompensated for their work (Sharma & Lal, 2024).
Ultimately, the best solution would be to establish an accountability framework such as all AI-generated content should be rechecked by human before being publish. Also, sensitive content should be subject to legal review, decisions and outputs must be traceable, and companies should choose suppliers that share the data they used to train their AI systems (Berry, 2025). In addition, it may be necessary to update intellectual property laws (Sharma & Lal, 2024).
AI in marketing: summary of risks and ways to address them
| Risk | Example | How to mitigate it | Sources |
| Algorithmic bias | DALL-E 2 associated the words “CEO” with white men | Diverse datasets, regular audits | (Berry, 2025) (Sharma & Lal, 2024) |
| Deception and loss of consumer trust | Undisclosed deepfakes | Clear AI labeling, deepfake surveillance | (Huh & Malthouse, 2025) |
| Copyright and ownership | Lack of copyright protection for AI outputs | Legal oversight, supplier disclosure, modernized intellectual property laws | (Berry, 2025) (Sharma & Lal, 2024) |
| Accountability gaps | A large share of users rarely verify what AI produces | Human review before publication | (Berry, 2025) |
FAQ
What does generative AI change in marketing?
AI is present through the entire marketing process, such as targeting, personalization, content creation and ad optimization (Gao et al, 2023).
Is it ethical to use AI tools for marketing?
Yes, but only to a certain extent. Using AI is ethical as long as it assists humans rather than fully replacing them as content creators, and it is important that humans review every output it generates (Berry, 2025).
How can a company use generative AI responsibly?
Companies can use AI responsibly by establishing audits and human review with transparency and clear policies (Berry, 2025; Sharma & Lal, 2024).
The ethical considerations of generative AI in marketing
In conclusion, generative AI can help us to accomplish remarkable things and allow us to push beyond the limits of our human capabilities. It can offer speed, personalization, and immense creative possibilities. Nevertheless, we must remain vigilant to ensure that it is used ethically. Therefore, it is important to pay attention to algorithmic bias, to the manipulation that reduce users trust through a lack of transparency, and to unresolved questions of ownership and accountability. These issues go beyond technology: they have a direct impact on brand image among consumers, on the ethical treatment of creative workers, and on who bears responsibility when something goes wrong. In addition, because AI technology is evolving rapidly while regulations are much slower to take shape, companies must take the initiative to set their own practices and govern how they use generative AI. Every company should audit its tools, disclose when it shares AI-generated content, and maintain human oversight. Ultimately, the ethical use of generative AI is the key to innovation, as it safeguards the credibility and longevity of both the technology and the companies that use it.
References
Berry, K. (2025, October 15). Ethics and AI: The risk no marketing leader can ignore. University of St. Thomas Newsroom. https://news.stthomas.edu/ethics-and-ai-the-risk-no-marketing-leader-can-ignore/
Gao, B., Wang, Y., Xie, H., & Hu, Y. (2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4).https://doi.org/10.1177/21582440231210759
Huh, J., & Malthouse, E. C. (2025). Advertising and Generative AI: How Can Advertisers Leverage New AI Tools? Introducing a Special Issue on Generative AI in Advertising. Journal of Advertising, 65(2). https://www.tandfonline.com/doi/full/10.1080/00218499.2025.2494973
Sharma, G., & Lal, R. (2024). Ethical considerations in the use of AI-generated images in advertising: Balancing innovation and responsibility. ICWMR-2024. SSRN. https://ssrn.com/abstract=4998132