The Untapped Ethics of Using AI in Social Media Marketing

• By Megan Allen
• 18-07-2025
• Social Media

Introduction

AI has changed how brands communicate and connect with everyone on social media. There are ethical implications of using automation and smart tools that marketers who are using them might be missing as they nail quick wins with content, targeting, and engagement.

This post shows a side of social media marketing that is rarely discussed: the day-to-day ethics of using AI. You will see what matters, what’s at stake, and how you can keep your head above water as AI continues to record-breaking changes in the way we engage socially.

The New Role of AI in Social Media Marketing

Artificial intelligence is now a central feature of social media. Marketers use AI to generate faster results with better responses while mirroring experience that resembles two people communicating directly with one another. But most never see the astonishing changes AI-driven tools are bringing about. Here’s a deeper look into how social media is using AI-driven tools and how it is transforming the social fabric between brands, influencers, and audiences.

Understanding AI-Powered Marketing Tools

Artificial intelligence has become the underlying engine behind much that happens on social platforms. These marketing tools are doing more than analyzing data—they are creating or predicting value, and interacting in ways that would have previously required humans.

Some common AI-driven tools making inroads in social media include:

• Human-like Ad Targeting: AI sorts through past data, networks, likes, stories, and user behavior to serve users ads they will most likely care about, based on user data.
• Chatbots & Virtual Assistants: Many brands use chatbots that either respond immediately (or potentially in seconds) to answer questions, or, to help answer basic service questions.These tools understand what the user actually wants and provide a real-time response as if they were a person (maybe even a better version).
• Content Generation: AI writes captions, pushes content out, and even generates original graphics or video. Some will even learn what worked with their audience and enhance the content to yield more clicks.

AI is not simply removing time, but also controlling what people see, how a brand will reach them, and ultimately what people will be talking about online. AI analytics tools will often identify trends (good or bad news) and notify brands so they can adjust before something bigger comes from it.

So what's the takeaway here?

• Efficiency: Marketers can do more with less time through the right audience.
• Personalization: AI develops ads, messages, and sometimes even content in real-time for individuals. This shows that the marketer recognizes the individual.
• Always on for engagement: Chatbots are constantly with no borders, time zones or restrictions, so at any time someone from the public can get the answer they want.

Again, while everything above is interesting, it is a new set of issues with a new set of implications for privacy and trust. The invisible hand of AI is pulling the strings and directing what we see through feed-writing, and most users do not realize these signals are unclear.

Changing Dynamics: Brands, Influencers, and AI

AI is taking away more than brands can do - it is altering who aligns with a brand when it comes to engagement with brand messaging.

For influencers and fellow audience members, these changes represent new rules and new ways of connecting.

Let's look how AI is changing engagement:

Brands are smarter: Brands used to need to guess the thought process for which influencer would be the best at promoting their product. Now AI can analyze millions of posts to find out who threshold influencers are, the level of engagement, and the percentage of engagement to align brands to the most relevant influencer that has the ability to influence their audience.

Influencers find new tools: Some influencers are now using AI to edit videos, analyze their audience, and even suggest posting times! With AI-generated insights, they can adjust the content they produce to increase their reach, sometimes without a full understanding of how the recommendations are generated.

Viewers gain better-targeted content: What is included in your feed is not an accident. AI is deciding, in real time every minute of every day what is included based on what you choose to view. The result is that feed capabilities create bubbles in which audiences are primarily provided content that aligns with their interests, and AI can shape the public sentiment relevant to what is trending or not.

On Instagram, TikTok, and Facebook we see these developments unfold every day:

• Instagram pushes posts based on what the AI perceives from user actions.
• TikTok offers a "For You" feed that uses AI so effectively that it will learn your preferred favorites in a matter of hours.
• Facebook uses AI when filtering word recommendations, ads, and also identifying potential spam.

The inclusion of multiple forms of AI could create a rapid feedback loop among users and the masses that influence them. Brands and influencers drive audience behaviors with AI, in turn their audience's responses will feed back into the advertising AI, shaping the next set of recommendations. Slow integration allows for change to be efficient, powerful, and sometimes, in the eyes of our users, unseen.

Who determines what is fair or ethical, when everything is determined by an AI model? This causes tensions over where paid content stops and utilitarian recommendations begin, ultimately creating confusion over what is recomended and what is necessarily recommended. With an ever-evolving AI, awareness of risks is the only way we can hope to reduce unseen risks.

Unexplored Ethical Challenges of AI in Social Media

Although AI is causing disruption in social media marketing, the complete extent of ethical challenges lie in variations that remain unexplored.While there is much talk about privacy and data breaches, fewer are examining the less evident ways that AI acts to shift trust, choice, and fairness online. Marketers and brands need to be conscious of what is happening beneath the surface. This section identifies several overlooked, yet troubling risks of using AI in social media, and aspects of the marketing practice that have severe, albeit 'small', impacts on users and brands.

Manipulation and Automation: The Thin Line

AI promises users better, more relevant content; however, its perils often lie within the transition from helpfulness to manipulation. Hyper-personalization means that ultimately each user has a feed curated just for them that 'seems to know' the user. Although the experience seems personal, it is often very easy for brands and influencers to shape opinions and behaviors, mostly without the user's knowledge.

Here are examples of the slippery slope that AI creates:

• Micro-targeted ads - AI can deliver ads so tailored to a user's intentions, mood, and activities, that they could be encouraging the user toward buying behaviors they did not intend.
• Influencer bots - Some accounts use AI to impersonate legitimate influencers, establishing trust and driving engagement as if real people.
• Automated content-flooding - Platforms could easily blast AI-generated comments or posts so freely, that they could drown out true concisely coordinated conversations, making it difficult to discern authenticity.

It can be hard to determine where just being inspired and being manipulated resides. AI tools can initiate emotional triggers by learning precisely what triggers spending, or drives the user to a choice. Not to mention using the ‘hyper-targeted’ approach raises questions about user autonomy and fairness as platforms increasingly automate direct outreach engagement messaging and influencer/brand partnerships.

For marketers and companies, it means being conscientious regarding the use of automation and machine learning. Are you focused on informing and connecting people, or simply getting clicks? As it becomes more difficult to draw the line between authentic engagement and manipulation, it’s never been more critical to say what you mean and stand by what you say.

Algorithmic Bias and Social Responsibility

AI isn’t always fair with how it gets there. In situations technology is deciding what content someone should see or what content, or ads, are targeting a group of individuals, any pre-existing patterns, biases or injustices could manifest the results in ways that original developers of those algorithms would not have intended.

The risks tend to manifest in a few major ways:

• Blindly reinforcing stereotypes- AI learns from past data, and therefore, is repeating past biases. An example of this is an advertisement tool that only shows tech jobs to men, instead of making them accessible to women.
• Continuing to marginalize- There may be certain algorithms that are actively hiding content related to marginalized communities while promoting content from majority communities, however unintentional that may be.
• Unequal targeting of ads- Certain users may receive better offers, or more helpful ads, depending on the background or behavior of those individuals.

Marketers who use AI algorithms to curate their content or targeting methods need to keep an eye open for these gaps. If your AI development team isn’t questioning bias in training data, blind spots in design, or implementing checks and balances throughout your process, you may have to deal with unequal outcomes. Algorithms may know plenty of things, but they have some limitations, and it’s best not to rely solely on them as knowing best in all scenarios.

Brands should take socially responsible actions by:

• Conducting an audit of their data- This means checking for bias and gaps in data to wondering where AI is accessed from.
• Demanding transparency- When choosing an AI tool, pick one that provides an explanation of how the algorithm decides.
• Receiving feedback from users- brands and developers will inevitably receive some push back and negative reviews related to biased or unequal results. Social responsibility involves accountability, not only for what AI is capable of doing, but for the social ramifications that may arise from its use. Numerical fairness is ethical thinking and smart practice.

Ethical challenges regarding AI in social media are very real, complex, and increasing; those who acknowledge these challenges sooner will build trust for the future.

Case Studies: Ethical Dilemmas Across Platforms

AI is not used the same way on every social network, and each platform's ethical conundrums are unique and shaped by how their AI is designed, how users engage with it, and what attracts their attention. By looking at real examples, we can begin to see how complicated these issues really are.

Facebook: Microtargeting and Political Manipulation

Facebook is the leader in AI-enabled ad targeting tools, and even though these tools were developed to sell more products and services, the implications involve risk and accountability in politics. For example, microtargeting is the opposite of a mass market and permits advertisers to segment audiences to very small groups of people and customize the messages they send to that group based on variables such as age, location, or even a person's emotional state at a moment in time.

Once again ensuring we are grounded in the real world, challenges arise when politicians or campaign groups are using AI that has the power to:

• Deliver different versions of an ad to different groups of people and obscure a complete picture of the ad for some and not others.
• Target groups of vulnerable people, such as older people or people showing signs of emotional distress, or simply targeting people with intoxicating social media ads disguised as worthy messages.
• Rapidly disseminate misinformation about politicians, celebrities, or brands by quickly recognizing what content regarding those subjects receives the most reactions and riding that wave.

The 2016 and the 2020 US elections provided just such an ethical study of this nature, where many combination of bot systems developed with AI were employed to propagate fake news stories, while microtargeting strategies for ads made it seem nearly impossible to identify misleading information to counter it.Users had no simple way to see what others actually saw, which turned their news feeds into filter bubbles.

There lies the ethical question: Should ANY GROUP get to change people's minds by convincing them to view advertising, using covert advertising technology, potentially hundreds of millions of users.

Instagram: Authenticity vs. Automation in Influencer Campaigns

Instagram’s recommendation engine fuels endless scrolling. Influencer partnerships are supposed to feel real. But AI now ghostwrites captions, generates content ideas, and can even spark engagement in the form of "auto-liked posts" or Reels comments to boost reach. This chips away at authenticity until users can tell what’s real. There’s also the mental toll—the endless chase Reels comments on trending videos strains well-being, and the urge to keep eyes glued to the screen becomes too easy.

• Perfectly curated posts and comments that seem genuine but are 100% programmed.
• Interactions with users that build emotional connections, sometimes without disclosure.
• Mass production of trendy images or opinions that shape what’s considered “cool.”
• Plus, comments on reels video raise a new wrinkle.

Then there’s the rise of deepfakes on Instagram. AI can now create photos and videos where someone’s face, voice, or body is swapped into another context. These fakes aren’t always obvious to viewers. Deepfakes can be used for fun, but they also raise concerns about fraud, reputation damage, and the loss of trust in what’s posted online.

Influencer culture on Instagram thrives on authenticity, but AI-generated accounts and deepfakes challenge this at its core. How can people trust what they see when technology can make anything look and sound real?

TikTok: Content Recommendation and Mental Health

TikTok does not have a legitimate business until it gets an addictive content recommendation engine. The artificial intelligence behind the platform's "For You" feed tracks everything watched, liked, and shared, constantly improving or ramping-up input for how to keep users hooked. The technology becomes so smart that users can lose hours of time without consciousness.

The artificial intelligence recommendation engine that TikTok uses can:

• Push users towards specific types of content (such as beauty or fitness videos) that negatively influence self- esteem.
• Exhibit trends or challenges that are not healthy or safe.
• Create "rabbit holes" where a user is served content after which they see more and more of the same type of content, influencing mood, outlook, and beliefs.

With young users, the risk is even greater. Some report a sense of anxiety or sadness after extended scrolling, while others cannot unplug. Irrespective of TikTok politely notifying or prompting a time limit, as they call it at the preset time limit the mandate is to ensure that as many eyes are on the screen for as long as possible.

For TikTok, the ethical dilemma is not only related to content, but also to the responsibilities connected to their artificial intelligence. When the artificial intelligence's role is to keep users in active and engaged status for as long as possible, is enough being done to protect mental and emotional wellbeing?

Comparative Insights: Lessons Learned Across Platforms

When you look at a platform like Facebook, Instagram, and TikTok, there are some patterns and similarities. Each respective platform has its own unique, specific issues, nevertheless, there are some ethical dilemmas that arose anytime artificial intelligence was intended to mediate information.

Common anxiety include:

• Manipulation: no time, its tiny nudges that can be through subtle microtargeted ads or addictive looping, and it even sometimes goes over the line.
• Transparency: people almost never know how/why certain posts or advertising ends up in front of them.
• Authenticity: the lines between what's real and what's AI-created is foggier than ever.
• User wellbeing: algorithms are developed for Attention not for user wellbeing or happiness.

In addition to these common concerns, each platform has its particular challenges:

• On Facebook, political manipulation, hidden ads are serious concerns.
• On Instagram, influence bots, deepfakes continue to erode a collapsing trust cradle.
• On TikTok, addictive algorithms and the basing derived from recommendations exposes users, particularly younger users, to heightened risk to their health.

These cautionary tales illustrate that as AI helps shape and dictate how marketers engage and represent brands on social media; platforms and marketers need to think ahead about their responsibilities. By showing what can happen when ethical lines are crossed, we can all work toward smarter, safer social media use for everyone.

Towards Ethical AI: Practical Solutions and Future Directions

Rather than waiting for regulation to guide brands, we can and should create a more sustainable model on our own. AI has already begun to change, and we have learned that the demands of advertising and marketing using AI will lead to more than just issues related to risks and harms when we think of marketing as any wide-reaching public engagement with the utmost future value subject to immediate scrutiny by the public. Marketing as discourse can paint our future and what meaning we assign that discourse and our decisions as brands into our future. Imagine the possibilities. We don't have time for AI, we need to act now.Embedding Transparency and Explainability into AI Systems

People want to know how decisions about what they see online are made, especially when an invisible system is curating the content they view or deciding which ads show up. For brands and marketers, providing transparency is not just a "nice to have" - it is an essential factor in establishing long term trust.

Here are a few actions to clarify AI decisions for users:

• Disclose automated interactions: For example, if there is a chatbot responding to questions or if an example of a virtual assistant is making recommendations for purchase, informing users up-front. Practicing transparency about the use of AI will alleviate confusion and build user confidence.
• Explain why content shows up: For example, platforms like Instagram and TikTok may be able to provide an easily identifiable feature that includes a clear "why am I seeing this?" Brands are encouraged to use the similar format, and inform their audiences about promotions or reasons they may have received an ad.
• Provide AI summaries: Brands can simply provide ½ to 1 page summaries of what algorithms do. An accordingly simple summary of data usage, ad targeting or personalization may provide the user with the simple knowledge of "data usage" or "ad targeting" or "personal information."
• User Chrome Extension for an easy dashboard UI system: Allow users to see what accounts provide the data for an AI system and allow options to change preferences, such as opt-out, when available.

Transparency is less about disclosing proprietary practices and more about treating people as partners rather than products. When users feel informed, they are more likely to engage and less likely feel manipulated.

Fostering Inclusive and Fair AI Practices

Every time AI is deployed within social media, there is potential for bias, but brands can take steps to mitigate this tendency. Fairness does not happen by accident; it requires substantive forward planning, to an ongoing framework of intention.

Consider doing the following in the ethical and equitable AI space:

• Diverse data entry: When using data, ensure it is diverse in terms of a combination of individuals, backgrounds, ages, and experiences. In your data is narrow, then your outcomes, any outcomes, will also be narrow.
• Bias testing: You need to run bias tests as often as possible to assess the risk of bias in your recommendations, targeting and/or generative content output, particularly in spotting patterns that support or harm specific groups of people (in relation to some aspects of the recommendations or ads).
• Create ethics review boards: Form an organization or committee that reviews the newest AI tools for risk in terms of fairness and inclusion.
• Make decisions readily apparent: It is critical to be open about the criteria upon which you made decisions about ad placements, recommendations or automated replies in your AI systems. This is especially true if those systems are making decisions that can impact people or groups differently from one another.

Frameworks like AI Fairness 360 (IBM) or Google's Model Cards can help supply frameworks for defining guidance. The most significant changes are always going to happen when brands take the initiative to share their experiences of failure to the tune of learning from them, asking for other people's feedback, and being honest with their own recognition of the aggressive aspects of their work they want to improve.

In situations where you identify bias or unfairness, once you acknowledge it, taking action quickly is the show of commitment from a brand to delivery on their ethical and equitable commitments, irrespective of who or what it is acting against. I would consider it quality problem. Correct it, you share it with your stakeholders, and you follow-up to ensure it is fixed.

Guidelines and Regulations: Shaping the Future

Policy has been and will lag behind technology no matter how many government regulations moral/ethical regulators put in place. Certainly, there is activity in regard to what a government's role is in guiding industries/brands in expectations about ethical AI, and finding opportunities ahead of this change, potentially circumventing issues, safeguarding reputation, or further gaining advantages competitively.

So, here's what we are seeing, and how brands can get ready:

• Data privacy laws: Laws, such as the GDPR (Europe) and CCPA (California) set clear requirements about how we collect, store, and use data from users. It is critical that brands understand the rules; and build compliance essential for their AI from the beginning.
• Platform standards: Social platforms are implementing their own standards, and rules, especially in the wake of "big" headlines. Expect guidelines around labeling AI-generated content, and clarity on handling political ads, and underage users.
• Responsibly and Ethical AI codes: There have been many different organizations such as the IEEE, Partnership on AI, and the IAB publishing codes around responsible AI. Many note important areas such as transparency, accountability, and rights of users.
• AI specific emerging laws: Specifically the EU's AI Act, and proposals in the US, will likely involve new regulations for oversight of AI in marketing, advertising, personalization, and content curation.

So, what does all this mean for the marketer?

• Conduct routine audits of your AI tools, and the rules of compliance.
• Educate your teams around responsible use of data, and responsible AI.
• Maintain engagement in industry groups, standards groups, or collective working groups to ensure you have the latest information, there are opportunities to contribute to future standards, and can engage with the best work.

Because following is no longer good enough. Brands who take privacy and fairness seriously can elevate standards for the whole industry, by prioritizing ethical actions, and using it as part of how they can differentiate themselves online.

Conclusion

It is more important than ever for the ethics of AI in social media marketing to be front of mind. AI does provide the potential to speed up delivery, which means marketing processes can benefit, but these benefits are not without risk, and cannot be ignored by marketing. When Brand's take actions that approach privacy, fairness, and responsibility, we are forming lasting trust, and building a stronger connection.

Taking these actions today means no excuses later. Keep inquisitive about your AI tools, make your practice transparent to your audience, and keep people at the heart of what you do. Thanks for reading, and I would love to hear your experiences, or if you have seen AI in social marketing cross the ethical line, your voice is essential, in shaping the future of this ever-changing landscape.