AI Ethics in Copywriting | 2025 Guide

Legal Issues, Practical Limitations, Ethical Concerns, Bias, Privacy, Job Threat, Best Practices

This guide is for informational use only. It is not legal advice. Please consult qualified legal professionals to address specific compliance requirements or legal obligations.

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TL;DR

Bias, privacy, misinformation, and job displacement have always been concerns in marketing and tech advancements – it’s no different with AI. Ethical AI use requires transparency, fairness, human oversight, and compliance with legal standards (e.g., GDPR, FTC guidelines). Brands risk losing consumer trust if AI-generated content is misleading or biased. Responsible AI practices – like disclosing AI use, monitoring bias, and ensuring data security – let marketers use AI’s power while keeping credibility and trust. This guide is not legal advice, but offers best practices, examples, and actionable steps to implement ethical AI marketing strategies.

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Is Using AI In Marketing Ethical?

It can be – ethical standards like transparency, privacy, and sustainability still apply to AI usage in marketing. 

Over 80% of marketers worldwide now use some form of AI in their digital strategies (Statista, 2024)​…but 94% of Americans voice some concerns about AI in marketing. 

They’re citing fears like:

• Misleading AI-generated content (39%), 
• Job displacement (34%)
• Privacy violations (32%) (WSU Carson College, 2024)​

The AI ethics conversation can hardly keep up with how fast AI is advancing.

AI ethics in marketing – in this context – is the responsible use of AI tools in creating and distributing marketing content. It’s fairness, transparency, and respect for consumer rights. 

This topic is urgent – brands risk losing trust and facing legal issues, and customers fear being misled or scammed. 

Marketers and copywriters are increasingly asking: Just because we can sell more with AI help… should we?

Here, we’ll discuss the major ethical challenges of AI in copywriting (from bias in algorithms to AI-driven misinformation), real-world case studies of AI ethics failures, expert insights, legal guidelines, and best practices. 

We’ll explore how the AI ethics conversation practically applies to marketing. We’ll cover:

• AI’s ethical limitations, like bias, security, and privacy concerns.
• AI’s practical limitations, like creative, monitoring, and data constraints.
• Legal considerations when using AI with copywriting and for marketing.
• A checklist of best practice guidelines when using AI within marketing.

We’ve created this guide to help copywriters and marketers navigate AI ethics. It’s not legal advice or official standards, but it aims to give you clarity.

Using AI in marketing ethically maximizes innovation while minimizing risk.

Let’s start.

What Are The Ethical Issues With AI In Marketing?

Key ethical challenges include bias, privacy and transparency, job displacement, and security risks.

Using AI in marketing can be ethical – but it requires clear guidelines and tight oversight. Ethical considerations act as guardrails to help AI-driven campaigns align with a brand’s values and societal norms. 

This matters for practical reasons: 

• Brands known for responsible AI use tend to enjoy stronger audience trust and loyalty​. 63% want creative companies to disclose their AI use, and 62% support clear labeling or watermarks (Baringa, 2025)
• Mishandling AI can damage a company’s reputation overnight. For example, Amazon’s (now scrapped) AI recruiting tool penalized resumes with the word “women’s,” as the system had taught itself to prioritize male candidates (Reuters, 2018)​.

How did that happen? Because most resumes in its database were from men. This case became a cautionary tale in tech and marketing circles – AI systems, if left unchecked, may absorb and amplify unethical biases from their training data.

Marketers must understand that AI is a powerful assistant, not a moral agent. 

In practice, this means handling AI outputs as proposals – to be reviewed and edited – rather than gospel. Without human oversight, AI-written copy might inadvertently produce offensive, misleading, or non-inclusive content. 

As Ina Fried, chief Axios tech correspondent, noted – companies aren’t letting AI run wild on its own: nearly all insist on keeping “a human in the loop” both for quality control and legal liability reasons (Axios, 2023)​.

Why do AI ethics matter so much in marketing? At the forefront, it’s about respecting other humans. It’s also because every piece of content you automate – an ad, an email, a social post – carries your brand’s voice and values. 

If an AI-generated ad inadvertently violates privacy or displays bias, it’s your brand that takes the hit. Ethical AI use protects you on multiple fronts: it safeguards your audience (to some degree) from harm, helps shield your company from regulatory fines or lawsuits, and upholds the quality of your content. 

“Companies with untrustworthy AI will not do well in the market” – Abhay Parasnes, CEO of Typeface

When there’s a risk that AI or algorithms are influenced by biased or unreliable sources — leading to misleading results — it may be better to avoid using AI altogether. You’ll see this in cases where AI is programmed with safeguards to avoid sensitive topics — like this legal query on Google.

Drawing AI efficiency into our human empathy/judgment helps maintain brand authenticity and prevents the “robot takeover” vibe that can alienate customers. Doing the right thing isn’t just moral – it’s a smart business strategy.

How Can Marketers Avoid Bias In Their AI Data Sets?

They must recognize when data lacks diversity, skewing results. When they don’t have access to key data, like AI training material, they need to focus on testing outputs, cross-checking sources, and refining prompts to minimize bias and confirm accuracy.

When we talk about data, we’re referring to anything from your website analytics to customer feedback, market research, AI-generated insights, and even the phrasing used in prompts. All of which can shape the accuracy and effectiveness of your messaging.

AI systems learn from data, and if societal biases fill the data, the AI can reproduce it. 

It’s a complicated discussion. How do you target an audience without bias? 

On the one hand, marketers should target ideal customers to provide real value to them.  It’s not ethical to mislead unqualified leads just to maximize sales. 

But qualifying should never turn into stereotyping.

In marketing, this can look like: 

• An AI tool unfairly favoring one demographic over another when targeting ads or crafting messages
• AI over-prioritizing old data, assuming previous best-selling products are always the most relevant, even if customer preferences have shifted
• AI chatbots assuming customer concerns based on past interactions rather than addressing unique, nuanced issues.
• AI-generated text producing culturally insensitive descriptions or examples.
• AI hyper-personalize emails to the point where customers receive offers that reinforce assumptions (e.g., sending all home improvement ads to men and all parenting content to women)

Bias is one of the most well-documented ethical issues with AI.

For instance, an AlgorithmWatch experiment found that their Facebook ads for a truck driving job were shown to men far more often than to women – inadvertently reinforcing gender bias around career opportunities (AlgorithmWatch, 2023)​. 

Ironically, even when an advertiser doesn’t directly select a target audience, the algorithm may discriminate anyway.

Such algorithmic bias isn’t always intentional, but the impact is real: it can alienate segments of your audience and invite public criticism or regulatory scrutiny.

AI bias can also manifest in more subtle copywriting contexts. If a generative AI has mostly seen content about certain groups portrayed stereotypically, it may produce taglines or product descriptions fed by those stereotypes. 

Imagine an AI generating copy for job recruitment ads. If its training data associates leadership roles with men and support roles with women, it might create ads that subtly frame managerial positions with masculine language like “assertive” and “dominant,” while using words like “helpful” and “nurturing” for assistant roles. This kind of bias can reinforce stereotypes without anyone explicitly programming it to do so.

“There’s a real danger of systematizing the discrimination we have in society [through AI technologies],” – Timnit Gebru, founder and executive director at The Distributed AI Research Institute (Salesforce)​

Mitigating bias demands proactive steps: 

• Using diverse training data
• Auditing AI outputs
• Setting rules in the AI system to avoid discriminatory language or targeting
• Educating yourself recognizing biased AI output

It’s a developing discussion. Practical advice on AI ethics about bias is, unfortunately, often more vague than actionable.

For example, Google’s has its own AI principles with a commitment to “avoid creating or reinforcing unfair bias”​ (Google Blog, 2018).

The good news is there are frameworks emerging to tackle AI bias. Many organizations now implement bias checklists (similar to a style guide) that marketers and developers use to review AI-generated content, ensuring it meets fairness standards before it goes public.

The goal is to let AI optimize performance without sacrificing inclusivity. It’s taking constant vigilance and a willingness to override or retrain the AI when it goes astray.

Do You Have To Disclose If You Use AI?

Disclosure isn’t always mandated – however, it’s good practice to be transparent about AI use, especially if it could mislead people – and you shouldn’t train AI on data collected without consent.

Whether you’re using AI in your marketing or not, (in the US) you still need to adhere to truth-in-advertising laws (FTC) and privacy laws (Office of Privacy and Open Government). That might involve disclosing your use of AI.

Modern marketing runs on data, and AI only heightens that appetite. AI copywriting tools and ad platforms thrive on user data to personalize content – but this raises major privacy concerns. 

Without ethical guidelines, an AI might scrape or utilize personal data in ways that cross the line. 

For example, in January 2025, class-action lawsuit alleged that LinkedIn had trained AI models with premium subscribers’ private messages. The lawsuit claimed LinkedIn had introduced a privacy setting that automatically opted premium users into data sharing with third parties without proper notice. This practice raised significant privacy concerns – users’ sensitive information, including business discussions and career prospects, was allegedly utilized without explicit consent. (The Times, 2025)

Marketers deploying AI must make sure their data sources and uses comply with laws like GDPR in Europe, (in the US) the Privacy Act of 1974, and (in the US) sector-specific rules like HIPAA in healthcare. 

For example, a hospital using AI to generate email newsletters shouldn’t feed the AI any protected patient health information (like identifying a patient and their diagnoses) unless it’s HIPAA-compliant – or they risk severe penalties.

In another example, an e-commerce company using AI to personalize marketing emails must be careful with customer data. If they train an AI model on past purchase histories without obtaining proper consent under GDPR, they could face legal action. GDPR requires explicit permission to use personal data, and failure to comply can result in hefty fines, as seen in past cases against major tech companies.

Transparency is another pillar next to privacy in ethical AI. It’s not just what data you use, but whether you come clean about AI’s role. 

Imagine receiving emails and reading blogs that were attributed to a company expert, only to later learn it was an AI-generated all along. 

You’re not the only one who would feel deceived. 77% of people want to know when content is AI-generated, and only a small minority are completely fine not knowing (Baringa, 2025)​.

As a result, transparency is becoming an expected norm: some jurisdictions (like California and soon others) require that bots or AI agents identify themselves as such, especially if they’re indistinguishable from humans (Loeb, 2024)​. The FTC in the U.S. has warned that using AI in a misleading way (for example, a deepfake influencer endorsement that isn’t labeled as such) could be considered a deceptive practice under advertising laws​ (FTC, 2023).

As a copywriter – it’s a good idea to disclose AI involvement in content creation when it’s not obvious. A simple note like “This article was generated with the help of AI” or an icon indicating AI content can go a long way in building trust. It respects consumer autonomy and also pre-empts the sense of betrayal that can occur if your audience discovers an AI’s role on their own.

Respecting privacy means only collecting and using data ethically and legally, and respecting transparency means telling people when AI is at play and, when possible, how it’s making decisions. 

We cultivate greater consumer trust when we’re transparent – a crucial currency in an era when data breaches and overwhelming marketing have made the public understandably skeptical.

Is AI Taking Over Copywriter Jobs?

Yes and no – AI is replacing copywriters in companies that don’t see the need for human oversight…or in those unwilling to adapt. But copywriters who scale their value – especially with marketing strategy – are finding AI to be a tool, not a threat.

AI’s encroachment into creative fields fuels the ongoing debate: Will AI copywriters eventually replace human copywriters? Should we use AI if it threatens other people’s jobs? 

However, AI is not a unique addition to this ethical concern around social responsibility. 

Similar fears cropped up in the Industrial Revolution of the early 19th century – machines replaced jobs, causing unemployment.

But because the fear of job displacement is old doesn’t mean it isn’t real: a 2024 survey shows nearly 60% of marketers are concerned about AI threatening their job security (InfluencerMarketingHub, 2024)​.

Entry-level copywriting tasks, from drafting product descriptions to A/B testing ad copy, can now be automated to a large degree by generative AI. It’s creating lots of understandable anxiety among creative workers. 

However, many experts believe AI won’t replace humans as much as streamline their current jobs. 

“It’s not about displacing humans, it’s about humanizing the digital experience” – Rob Garf, VP and GM at Salesforce Retail (Salesforce, 2023)​

What can marketers and copywriters do? 

Do we owe others jobs, and do others owe us jobs? 

It’s another complicated facet to the AI ethics conversation. However, there’s several ways we believe we can positively contribute:

• AI shouldn’t replace humans where it isn’t competent. Don’t use AI to create false, plagiarized, or misleading copy – ethical responsibility is still yours, even if errors were accidental.
• Keep talking about – and proving – how humans handle AI-resistant tasks, like avoiding bias, adding emotional intelligence, and validating sources.
• Continue upskilling and making AI discussions accessible. Staying engaged in the conversation helps copywriters and marketers grow, stay valuable, and avoid job losses.

We agree with – and are encouraged by – Dr. Ka-Fu Lee’s perspective on AI:

“Humans need and want more time to interact with each other. I think AI coming about and replacing routine jobs is pushing us to do what we should be doing anyway: the creation of more humanistic service jobs.” — Dr. Kai-Fu Lee, chairman and CEO of Sinovation Ventures (Salesforce)

What Are The Risks Of AI In Marketing?

AI is not inherently secure or reliable – it can be exploited if not properly designed and monitored. 

AI’s training data needs to be high-quality and gathered with consent. Security issues and reliability problems can still occur unintentionally. 

We’ve seen some real world examples with:

• Tech giants using copyrighted material (without consent) to train AI systems. Creators’ works are exploited without proper authorization or compensation. Companies like Getty Images have sued AI firms for training their models on their copyrighted images without permission (AP News, 2023). 
• Writers creating inaccurate AI-generated content. Several media outlets have faced backlash for publishing AI-generated articles without proper oversight. For example, a reporter in Wyoming used generative AI to create fake quotes and stories for his journalism (NBC News, 2024).
• People manipulating a company’s AI campaign. You might create a marketing strategy, but internet trolls can derail it. We saw this with Burger King’s TV ad designed to trigger voice assistants by asking “OK Google, what is a Whopper?” – which then caused Google Home devices to read off Wikipedia. Pranksters quickly edited the Wikipedia page to say wild misinformation (like Whoppers contain cyanide). Burger King’s clever AI trick backfired with a reputation scare (BBC, 2017)​.

Copywriters and marketers can help clients avoid security and reliability risks. This is a huge value-add to your skills, since it lets your clients enjoy AI’s benefits while protecting themselves.

Here’s how we can help:

• Fact check claims, sources, and stories generated by AI. If you’re using AI for writing support, don’t let fictional testimonials, misquotes, or hallucinated sources slip in.
• Don’t give AI protected data. Don’t feed sensitive information or data collected without consent into an AI tool you’re using.
• Don’t feed AI data that could distort its outputs. For example, if you’re uploading web analytics to ChatGPT but know your data has gaps (like tracking only high-intent users while missing drop-offs) AI might generate overly optimistic (and misleading) insights. If an e-commerce brand trains AI on past sales data but excludes product returns, the AI might suggest overly aggressive marketing strategies based on incomplete success rates.
• Have guardrails. Use plagiarism checkers. Don’t use AI as a shortcut for poor planning – allow time for manual checks.
• Test AI before you approve outputs – or integrate it into your systems. You want to use Claude or Jasper to speed things up? Try it out. Do heavy quality control. This helps you clarify guardrails or find better AI tools – or skip them if they’re not effective.

Security-wise, marketers should also be aware of data protection in AI tools. If you’re using a third-party AI platform, is the customer data you input into it secure? 

There have been instances of AI services inadvertently leaking sensitive info because of how they were logging user queries. Ethical marketing teams will vet AI vendors for strong security practices – end-to-end encryption, secure data storage, and clear policies that the data input won’t be used to train models without permission. 

Moreover, consider worst-case scenarios: what if your AI content generator goes down or starts behaving unpredictably right in the middle of a campaign? 

Many AI issues can be fixed before launching copy or campaigns. But for customer-facing AI, (like chatbots, voice assistants, automated emails) companies need a backup plan. 

Being able to switch to human-written content on short notice is part of responsible AI use. You can also program your AI chatbot with a blacklist of offensive terms and a fallback response like, “I’m sorry, I can’t help with that request,” if it detects a problematic input. Just as you’d have a crisis PR plan for a social media gaffe, have a plan for AI glitches. ​

We saw this with Microsoft’s Tay chatbot fiasco: internet trolls bombarded the AI Twitter bot with hateful messages, causing Tay to start outputting offensive tweets and forcing Microsoft to shut it down within 24 hours (Microsoft, 2016)​.

A great way to quality control customer-facing channels is to simulate possible misuses or errors and build in filters. Responsible AI deployment in marketing demands the same diligence as any product rollout: QA testing, security reviews, and continuous monitoring. 

It may not be as flashy as launching an AI-written slogan, but this behind-the-scenes work is what keeps your innovations from turning into ethics nightmares.

Why Won’t AI Replace Copywriters?

AI can mimic deep creativity and emotional nuance – but without heavy context and direction provided by a human, it struggles to apply them effectively in marketing.

Copywriters bring the magic of human experience, niche expertise, and intuition about real pain points.

New research is showing that AI-generated ads (both “low” and “high” quality) aren’t activating memories as strongly as with traditional ads (NIQ, 2024). Weaker activation implies the content doesn’t connect well with consumers’ memories, making them less likely to take action.

Additionally, a recent study found that AI-generated product/service descriptions were consistently less popular among consumers, which indicates a growing backlash and disillusionment with the technology (Taylor & Francis, 2024).

Some AI is very competent at pulling together ideas and speaking to the heart. But what creativity and emotion do humans add that AI can’t generate on its own?

• Copywriters know what subtleties turn off their target audience. Copy can’t persuade if it starts by making the reader doubtful or offended. For example, a human familiar with the audience might spot AI-written landing page copy that unintentionally sounds like a multilevel marketing scheme.
• AI needs a creative director. Choices have to be made. A company can’t do all the marketing options out there. It can generate blogs, but where do those blogs fit in a funnel? A real person has to decide.
• Someone has to steer the course correcting and risk taking. The market changes fast, so AI can’t run on autopilot. For example, when ChatGPT became popular for web searches, it shifted traffic from Google to Bing. Since they rank content differently, marketing strategies had to adapt. Humans need to set checkpoints to keep it on track.
• AI lacks the ultra-niche expertise that makes copy stand out. Great copy connects through specific experiences. For example, AI might help a doctor write an SEO-friendly blog on congestive heart failure, listing common pain points like fatigue, swelling, and diet restrictions. But a niche copywriter, through knowledge or interviews, could integrate  a common frustration – many patients can’t breathe comfortably laying flat in bed and need to sleep in a recliner. Adding this detail makes the copy instantly more relatable and impactful.
• AI needs training and/or heavy editing to produce the right content. It can create highly relevant copy – but only with proper context. Who provides the brand guide? Who checks if AI follows it? Humans do.

With AI flooding marketing, copy is starting to sound the same, and consumers are noticing. Some of the same phrases and formatting are showing up in blog intros and emails. 

The content creation may have been more efficient, but it costs brands their uniqueness and weakens their authority. What’s the point of a brand if not to protect those qualities?

Human creativity and empathy keep them intact.

“The most accurate tool for measuring trust is human intuition.” – Simon Sinek

What Is The Problem In AI In Marketing?

AI has two major constraints: data quality and resources. It can only generate content based on the data it’s trained on, and running AI requires significant energy and computing power.

Here are some of the data constraints businesses are facing with AI:

Training advanced AI models requires massive datasets.

If your data is limited or poor quality, the AI’s output will be limited or poor. 

Large companies tend to have an advantage. For example, Mondelez International, the maker of Oreo and Ritz Crackers, collaborated with Google, Accenture, and Publicis’s Sapient to build an AI platform that uses historical advertising and marketing content. They have tremendous amounts of data, so this project improves marketing by using past campaign successes to shape future strategies. (​WSJ, 2024) 

On the other hand, a small business with limited customer data would probably find AI’s insights ineffective because it lacks enough examples to learn useful patterns. For example, the business could feed AI all of its email campaign analytics for insights, but if their email list is small, and they’ve only done a few email campaigns, the data “insights” may skew in a direction that’s not really representative of helpful direction for the future.

Many available datasets (like large pools of internet text) carry historical and cultural biases.

Even if it doesn’t overtly produce unethical content, an AI might just be out of touch with certain segments if those segments weren’t well-represented in the training data. 

For marketers aiming at niche or diverse audiences, this is a practical shortcoming – the AI might just not “get” the vernacular or values of that group, requiring manual tweaking.

For example, if a business serves entrepreneurial moms who prioritize hard work and efficiency, AI might assume they want messaging about chaos and needing a break – when in reality, that doesn’t resonate with them at all.

Your AI might be “hungry” for data you’re not allowed to feed it.

Data privacy laws effectively limit data usage. You may have a trove of consumer data that could fuel AI insights, but laws like GDPR or California’s CCPA might restrict using it without consent.

In such cases, AI ethics and compliance force a hard limit: you simply exclude certain data from AI training (for instance, anything that can identify an individual without their permission). This can make the AI slightly less “smart” about individual personalization, but it’s the right thing to do and avoids legal risk. 

AI often needs ongoing access to updated data to stay relevant.

A copywriting AI model trained on internet data up to 2023 will not know about cultural trends or news from 2024, 2025, etc. If not updated, it might produce content that feels stale, makes references that are no longer accurate, or worse, hallucinate “facts.” 

Another constraint is the cost and computational resources associated with advanced AI. 

You can use cloud-based AI copywriting tools without knowing what’s under the hood. But for organizations thinking of building their own models (or even fine-tuning existing ones on their own data), the investment can be significant. Training a language model can cost millions of dollars and consume huge amounts of energy​ (CACM, 2024).

AI usage has environmental impact.

Running AI models, especially large ones, can have a surprisingly heavy carbon footprint. For example, training GPT-3 was estimated to consume as much energy as a single American household does in 120 years​ (Harvard Magazine, 2024).

A popularly circulated post from the Wall Street Journal reported that a mere 100-word email generated by ChatGPT would consume about a bottle’s worth of water (WSJ, 2024).

However, Sam Altman (CEO of OpenAI) stated in his blog that one ChatGPT query only uses “roughly one fifteenth of a teaspoon [of water]” (Sam Altman blog, June 10, 2025).

What do we do as users who want to be mindful of their own AI energy use?

Unfortunately, it’s hard to measure the energy a request uses since it varies by type – like how image generation usually takes more than text. Different AI models also use different amounts, and companies don’t always share their energy data. (Nature, 2025)

There’s also AI usage that’s beyond our control. For example, the AI summary that sometimes appears before Google or Bing search results can’t be disabled.

Marketers, especially those at eco-conscious brands, might find this at odds with sustainability values. Does the benefit of the AI outweigh the environmental cost? 

Here are some practical responses a copywriter or marketer can take in regards to resource and data limitations:

• If relevant, consider using pre-trained models hosted by big providers (specifically who are optimizing for efficiency) rather than training from scratch. 
• Limit the complexity of AI you use to what you actually need. You might not need the absolute bleeding-edge model to write product descriptions. For example, you could opt out of using the highest models of ChatGPT for the simpler writing tasks.
• Don’t use AI at all for unnecessary tasks. Don’t summarize emails you weren’t planning to read anyway. Be mindful when generating images.
•  Know when the “knowledge cutoff” of your AI is. This helps you understand AI’s limits and avoid mistakes, like asking a model trained until 2024 for details on a 2025 event.
• Give AI with accurate, relevant, and properly collected data that aligns with your analysis goals. For example, if you’re using AI to analyze customer feedback, don’t feed it a mix of outdated surveys and social media comments without filtering for relevance. 
• Watch for bias in AI outputs. Know your audience firsthand – not just through AI’s assumptions about them.

AI may be new, but quality data and responsible resource management have always been relevant. 

Don’t feed AI bad data, and don’t use it recklessly.

Is AI A Threat For Marketing Jobs?

Yes, for ones who resist change – no, for those who adapt, monitor, and collaborate with AI.

AI is very competent – and it’s getting better at the human elements of being creative sounding empathetic. Understandably, it can be alarming to see it match and surpass core human skills.

For example, in a turn many didn’t see coming, Allstate (the insurance company) found that their AI reps were coming across more empathetic than their human reps. (WSJ, 2025)

How is that possible, and why is that the case if AI tends to struggle with emotional intelligence?

• The AI-generated emails help handle the back-and-forth between customers and claim representatives. It’s often frustrating, and prior to the AI generated email support, reps would often use insurance jargon that didn’t help the situation. 
• Once the AI models were given company specific terminology and training, they could take it from there. Ironically, it was the lack of sensitivity to frustration that was helping boost the “empathetic” perception.

Turns out, humans are still heavily involved in the maintenance and monitoring of AI.

• A human claim agent still looks over the AI generated email to verify it’s accurate – they’re just not having to write it.
• Humans had to train the AI models on the Allstate standards.

It would certainly be convenient, but you can’t just “set and forget” these kinds of tools. Once you deploy an AI system – whether it’s a chatbot, a content generator, or a recommendation engine for products – it requires continuous monitoring and maintenance. 

For copywriters and marketers, that means there’s now a huge management need for effective AI collaboration.

• Business goals shift, your customers evolve, external events (like a pandemic or a social movement) can suddenly change what messaging is appropriate. A static AI model won’t automatically know to adjust for these. Humans have to update the system or its content guidelines. 
• Continuous oversight is what ensures the AI doesn’t drift into problematic territory over time. Think about an AI moderation system for a community forum – it might work fine for a while, but as slang evolves or trolls find new loopholes, the AI might start missing things or falsely flagging innocuous content. Regular reviews and updates are needed to keep the AI effective and fair.

This AI management need is needed for both small scale and large. 

For solopreneurs, managing AI-driven marketing (and keeping it off a company’s plate) is a tremendously valuable service. For example, they can handle complex pillar pages and blog production with AI, offering companies high-quality content without in-house effort.

At a larger scale, AI creates opportunities in prompt engineering, data analysis, and quality control. While AI can generate copy, human expertise is also essential for refining prompts that guide the copy, analyzing results that guide strategy, and ensuring quality.

In the marketing copy context, maintenance might involve routine checks of AI-generated content quality. It’s similar to software updates. Practically, this looks like:

• AI governance teams. Many companies are now creating teams, or at least assigning responsibility to someone (like a content manager or AI ethicist) to periodically audit AI outputs. They might randomly sample 100 AI-written social media posts per month to ensure none contain bias or errors, for example. If issues are found, that triggers a retraining or re-adjustment of the model or the prompts they’re using. Without this, problems may only surface when there’s a public mistake – at which point damage is done. 

• Performance monitoring. AI may not degrade in the way mechanical parts do, but its performance relative to the world can degrade. For example, if an AI was picking the best email subject lines based on open rates, and suddenly email habits change (maybe people start ignoring anything that looks AI-written due to overuse in the industry), your AI’s old tricks might stop working. You’d notice open rates dropping and need to intervene, perhaps by giving the AI new training data of what currently works or, in some cases, turning off the AI until it can be improved. This happened in some companies where automated social media posting AI began to underperform because social network algorithms changed – the content that was once favored (maybe overly-clickbait headlines) started getting demoted. The fix was to retrain the AI on new engagement data. 

• Covering security patches. If an AI platform has a vulnerability (say someone found a way to prompt your AI chatbot into revealing confidential info – a prompt injection attack), you need to update it. This might involve applying vendor patches or improving your prompts to sandbox the AI better. 

From a resourcing perspective, companies should plan for the lifecycle costs of AI. Budget and time for team members to do these reviews, the IT costs for updates, and the possibility of needing to replace an AI model after a couple of years if it’s out of date. 

In summary, treat your AI like an employee or a team member – it needs supervision, evaluation, and occasional training to keep performing well. 

The question “Is AI a threat to marketers?” is often answered by pointing out that AI works best with marketers involved. 

It’s like asking if a great employee will threaten a company by taking other employee’s jobs. With unlimited capacity to work, it might be able to handle multiple people’s jobs, but it still needs management to direct it and oversee quality.

What Are The Ethical Considerations In AI-Powered Marketing?

Make sure your AI-driven campaigns aim to provide real value to consumers (social benefit), regularly test your AI for biased outcomes (fairness and safety), keep humans in charge of final decisions (accountability), and respect user privacy at every step.

Thankfully, we’re not navigating AI ethics alone – there are established best practices and evolving frameworks to guide responsible AI use in marketing. 

One cornerstone is Google’s Responsible AI Guidelines, which are based on seven key principles (Google, 2025). These principles include directives to: 

1. Be socially beneficial – AI should contribute positively to society. Provide real value to customers and don’t spam to manipulate algorithms.
2. Avoid creating or reinforcing unfair bias – AI must be designed to prevent discrimination. Collect, input, and analyze data responsibly.
3. Be built and tested for safety – AI should be reliable and secure.
4. Be accountable to people – AI systems must remain under human oversight.
5. Incorporate privacy design – AI should protect user data and privacy.
6. Uphold high standards of scientific excellence – AI should be developed with accuracy and innovation.
7. Be made available for uses that align with these principles – AI should not be misused for harm.

For example, before launching an AI-personalized ad program, you might run an AI ethics checklist (we’ll provide one at the end of this page).

Beyond Google, many organizations and governments have proposed ethical AI frameworks relevant to marketing. 

The European Union’s upcoming AI Act is a landmark regulation taking a risk-based approach (European Commission). It will outright ban certain AI uses deemed “unacceptable risk” (for instance, AI for social scoring or exploitative surveillance) and impose strict requirements on “high-risk” AI (which could include some marketing uses like creditworthiness assessments for marketing financial products) (Forvis Mazars).

For most marketing and copywriting applications – likely considered lower-risk – the AI Act will still require transparency (e.g., labeling deepfake content) and accountability from providers. 

Another framework to note is the FTC’s guidance in the U.S. – while not an AI-specific law, the Federal Trade Commission has been actively cautioning marketers that truth-in-advertising laws apply to AI-generated content just as much as traditional content (FTC, 2023).

Their advice boils down to: don’t make false or unsubstantiated claims about what your AI can do, and don’t let your AI put out false claims about your product (Fenwick, 2024)​.

In one case, the FTC penalized companies for marketing “AI-powered” tools that didn’t really work as advertised. The lesson? Claiming your product is “AI-driven” won’t save you if the underlying promises aren’t met.

On an industry level, several consortiums and companies have published AI ethics guidelines. 

For instance, the Partnership on AI, a multi-stakeholder group, offers resources on fair, accountable AI (Wikipedia). IBM, Microsoft, and other tech companies have also shared their internal ethics guidelines publicly, which often include things like human oversight, explainability, and fairness.

While these can be a bit high-level or technical, we marketers can extract relevant nuggets. 

For example, one common principle is transparency – not just disclosing AI use to consumers, but being transparent within the company about how AI decisions are made. A marketing team or copywriter might adopt a policy of documenting every AI tool they use, what data goes into it, and what logic it applies. This creates an audit trail. 

It’s also helpful if something goes wrong – you can quickly trace the cause (perhaps a flawed dataset) and correct it. 

Additionally, it’s worth considering certifications or audits if a company’s building AI software. There are emerging services that will audit your AI (similar to a security audit) for biases or privacy issues. Engaging in an external AI ethics audit can demonstrate your commitment to accountability. 

It can also be a selling point to clients or customers. It may become a competitive advantage as consumers grow more concerned about AI practices.

Best practices for ethical AI in marketing are evolving as the tech capabilities evolve, but themes arise. We can follow formal guidelines (like Google’s and the EU’s), adhering to legal requirements (FTC, GDPR, etc.), and using common-sense ethical design (fairness, transparency, human oversight). 

Baking these into your AI projects from the get-go – not as an afterthought – sets the stage for AI initiatives that are innovative and responsible. 

We’ve consolidated these into a handy checklist.

Ethical AI Checklist for Marketers and Copywriters

• Bias and Fairness: Have I checked the AI output for biased or stereotypical content? (Collect accurate data to avoid skewed analysis; run tests to see if any group is being excluded or portrayed unfairly.)
• Transparency: Am I disclosing that this content or interaction is AI-generated where appropriate? (For example, clearly label chatbot interactions as AI, include an AI disclosure footer in AI-written articles, or clarify in your contract if you use AI.)
• Privacy and Data: Am I complying with privacy laws and respecting user consent in the way I’m using data? (Only use customer data that you have permission to use; avoid feeding sensitive personal data into external AI tools; anonymize data when possible.)
• Accuracy and Truthfulness: Have I fact-checked the AI’s claims or statistics? (Verify any factual statements with a reliable source. If the AI provided a number, quote, or source, ensure it’s correct or replace it with verified info.)
• Human Oversight: Is a human reviewing and approving the AI’s output before it goes live? (Set up a workflow where AI-generated copy is edited or at least reviewed by a team member, especially for high-stakes content.)
• Accountability: Is it clear who on the team is responsible for the AI’s actions and outcomes? (Assign an owner for each AI tool or campaign who will monitor performance and handle any issues that arise.)
• Compliance Checks: Have I considered relevant laws/regulations? (e.g., Does my AI marketing email comply with FTC guidelines and not sound deceptively “human”? Does using an AI voice in an ad comply with any state laws requiring disclosure?)
• Security: Am I protecting the AI from misuse and safeguarding any data it uses? (For example, keeping AI usage in house and not customer-facing, implement content filters to prevent the AI from outputting offensive content if prompted maliciously; ensure APIs or tools are secure.)
• Continual Monitoring: Do I have a plan for monitoring the AI’s output over time? (Schedule periodic audits of content quality and correctness; set up analytics to catch unusual trends that might indicate a problem.)
• Opt-Out and Feedback: Am I giving users a way to opt out of AI interactions or provide feedback? (For instance, allow users to request a human agent if talking to a chatbot, and treat that request respectfully. Collect user feedback on AI content to learn if it’s hitting the mark or annoying people.)

Remember, the goal isn’t to make AI use cumbersome – it’s to make it conscientious. We want to build customer trust and treat them well.

Is It Legal To Use AI For Copywriting?

AI copywriting isn’t exempt from the law: marketers still need to comply with relevant laws like the GDPR, copyright and intellectual property laws, and FTC guidelines.

Navigating the legal side of AI ethics is a critical part of marketing responsibly. Several regulations and guidelines directly impact how marketers should use AI tools:

United States Privacy Laws

Certain laws protect certain customer information, so you can’t just collect any customer data and feed it into ChatGPT for analysis.

For example, the Privacy Act of 1974 governs the collecting, handling, and sharing of an individual’s data. Similarly, the Children’s Online Privacy Protection Act (COPPA) of 1998 outlines how a child’s personal information online can’t be collected without the parent’s consent. Health Insurance Portability and Accountability Act (HIPAA) protects personal health information.

Even when information is collected legally, there’s still privacy concerns around communication, like marketing emails. An extremely relevant law around marketing AI ethics is the CAN-SPAM Act, which sets rules for commercial messages. You can’t simply send commercial messages without clarifying it’s an ad or solicitation, with no address (or an invalid one), with no way for the recipient to opt out.

General Data Protection Regulation (GDPR)

GDPR is the EU’s stringent data privacy law, and it has global reach for any company handling EU residents’ data.

 For AI in marketing, GDPR means you must have a lawful basis (like explicit consent) to use personal data for targeted advertising or content personalization. If your AI copywriting involves profiling users (e.g., tailoring email content based on user behavior), you need to inform users and, in some cases, allow them to object. GDPR also enshrines principles like data minimization (only use what you need) and purpose limitation (don’t repurpose data in unexpected ways). 

For example: If you’re using an AI to write personalized product recommendations, ensure the data feeding that AI (purchase history, browsing data) was obtained with proper consent and is used in line with what the user was told. 

Violating GDPR can lead to hefty fines – in the tens of millions of euros – so it’s not just an ethical lapse, but a serious financial risk too. It’s especially important to consult your legal team when deploying AI that touches user data in Europe.

Federal Trade Commission (FTC) Guidelines (USA)

The FTC oversees truth-in-advertising in the United States, and they have made it clear that using AI doesn’t exempt anyone from these rules. 

In 2023, the FTC warned businesses that it would crack down on deceptive AI marketing – whether that’s overstating what AI can do (“This AI will write perfect content for you, guaranteed!” – an unsubstantiated claim) or using AI in ways that mislead consumers (like a deepfake influencer endorsing a product without disclosure)​ (Global Inside Tech, 2024)

In a recent real-world example, the FTC brought an action against Rytr (the AI writing software) for its review generation tool (FTC, 2024).

Be transparent, be truthful, and don’t use AI to do anything you couldn’t legally do yourself. If an ad would be deceptive coming from a human, it’s still deceptive coming from an AI. Also, if your marketing AI collects user info (maybe a quiz that feeds into an AI for a product recommendation), the FTC’s focus on data security means you must safeguard that info from leaks or hacks​ (FTC, 2025).

Emerging State Laws

In the absence of a comprehensive U.S. federal AI law, some states have begun legislating on AI in consumer interactions. 

California’s Bolstering Online Transparency (BOT) law requires that automated online accounts (bots) that try to sell something or influence a vote must disclose that they are not human, if failing to do so would be misleading. In 2019, California specifically made it illegal to use a bot to communicate with someone in a way that impersonates a person, for the purpose of promoting a sale, without disclosing it’s a bot​ (Loeb, 2024).

More recently, as cited above, Utah passed a law mandating that if a user asks an AI if it’s human, the company must disclose the truth (Utah SB 149)​ (Loeb, 2024).

These laws reveal a legal trend: misrepresentation by AI = fraud, just like misrepresentation by a human. 

For marketers, that means you should build in disclosure from the start (don’t wait for a user to ask – proactively make it clear). It’s also wise to keep logs of AI-driven communications, so if ever challenged, you can show you weren’t trying to deceive. Keep an eye on states like New York or others that might introduce AI disclosure or data protection laws – this area is evolving.

HIPAA (Health Insurance Portability and Accountability Act) (USA)

If you’re in healthcare/mental health marketing or touching any medical data, HIPAA is critical. It protects personal health information (PHI). 

Although HIPAA was mentioned earlier, we highlight it again because many copywriters unknowingly handle protected health information using non-HIPAA-compliant tools. Using AI to generate health-related content or target healthcare services gets tricky because any patient data you use must be handled per HIPAA rules. 

For instance, say you use AI to personalize email newsletters for a therapist. If you include info like the diagnosis and medications someone is on (which is PHI), you must have the patient’s explicit authorization – saying “they told me it was fine to share their story” may not hold up in court. The AI tool you use also must be a “business associate” under HIPAA (with a signed agreement to handle data properly) (LegalOn).

Many general AI copywriting tools are not HIPAA-compliant, which means you shouldn’t feed them raw patient data. It’s the same with non-AI tools. For example, a free @gmail email isn’t HIPAA compliant – a client should not send you protected patient information. A breach of PHI due to AI misuse could lead to major fines and penalties. 

The ethical route (and really the only legal route) is to anonymize or aggregate data such that it’s not identifiable, or get proper consent and use specialized compliant platforms. When in doubt, err on the side of privacy – it’s better to have slightly less personalized content than to accidentally leak someone’s medical info through an AI-generated marketing message.

Copyright and Intellectual Property

This is another legal angle – not exactly a regulation like the above, but certainly an area of legal risk. 

AI-generated text and images have raised questions about copyright. As a marketer, if your AI copywriter tool produced a tagline, who owns it? Generally, the user or company using the tool will claim ownership of the output (many AI tool providers state that you own what you generate). 

However, if the AI plagiarized without you realizing (e.g., it regurgitated a sentence from its training data verbatim), there could be copyright infringement issues. 

It’s wise to use plagiarism checkers on AI-written copy that’s meant for publication. Also, avoid prompting AI to “write in the style of [Specific Author]” and then publishing that result, as it might infringe on that author’s style rights – or at least tread ethically murky waters. 

Authors are already taking legal action. In 2023, numerous authors, including John Grisham, George R.R. Martin, and Jodi Picoult filed class action lawsuits against OpenAI for copyright infringement (Author’s Guild, 2023).

With images, there’s active debate: if you use a generative AI to create an image for an ad, ensure the model is legally cleared (some early AI image tools were trained on artists’ works without consent, leading to lawsuits). 

Also, similar to unintentional plagiarism with AI text, AI images can accidentally generate copyright-protected products, characters, and symbols. Make sure your images don’t have, for example, a Disney character look-alike or Coke can.

In summary, while the tech is new, existing laws still apply and new laws are coming.

Marketers should treat AI as another facet of their operations that needs legal compliance checks. This might mean involving legal counsel earlier in campaign planning when AI is involved, or updating your company’s compliance training to include scenarios on AI. 

The message from regulators is consistent. Prioritize consumer protection, fairness, and honesty, no matter if a human or an AI is doing the work. Keeping things legal also builds a reputation as an ethical, trustworthy brand.

Do Consumers Trust AI?

​Consumers are often skeptical (or even biased) against AI-generated content – however, they’re more open to it when brands are transparent about AI use and prove that it benefits the customer experience.

Current research shows a mixed picture: consumers appreciate some AI-driven conveniences, but they also harbor skepticism. 

Over 71% of consumers are concerned about trusting what they see and hear due to AI, yet almost 83% say that labeling AI-generated content should be required by law. (Quirks, 2024).

That’s a striking stat – people want transparency, yet it might actually reduce engagement, because people worry AI content could be fake or inauthentic. 

But the alternative – hiding AI’s involvement – is not a solution either (and as we discussed, doing so could violate emerging laws and ethics).

Recent studies also indicate that consumers often perceive AI-generated ads as less engaging and more “annoying,” “boring,” and “confusing” compared to traditional advertisements. (​NIQ, 2024).

This sentiment suggests there may be a negative halo effect, potentially diminishing consumer perceptions of both the advertisement and the brand itself.

We saw this with the viral Ghibli-style Lord of the Rings trailer recreated by frame-by-frame by PJ Ace (YouTube, 2024). This sparked a wide-spread trend of using ChatGPT’s image generator to turn selfies and memes into a Hayao Miyazaki-inspired anime.

The reaction was divided.

Many people were delighted and thrilled to easily create images in the beloved Studio Ghibli style. On the other hand, many artists, animators, and business owners felt it was a gross misuse of copyrighted work – disrespecting the effort behind the styles Miyazaki (Ghibli’s co-founder) and Peter Jackson (director of the early 2000s Lord of the Rings films) worked hard to develop.

As a result, many businesses and industry leaders who shared Studio Ghibli-style images of themselves were met with backlash in the comments. Companies that joined the trend were suddenly bombarded with accusations that they condoned stealing people’s art.

Trust is the backbone of any brand–consumer relationship. If your audience doesn’t trust AI content, or worse, loses trust in your brand for using AI, that’s a serious problem. 

The solution, then, is to intentionally build trust around your use of AI. 

How a brand uses AI can itself become part of its values and story. Prove to your audience that AI is making their experience better; don’t just make claims. 

• Make great content. Clarify the AI support used and why it was used. For example, Patch, a hyperlocal digital news platform, utilizing AI-generated newsletters. These newsletters aggregate information from verified sources, allowing Patch to efficiently deliver tailored content. This strategic use of AI made Patch into a comprehensive hyperlocal information service, significantly boosting its revenue – but also expanded its reach from 1,100 to 30,000 U.S. communities (Axios, 2025). This means AI helped deliver a higher value newsletter for the customer.

• Show why AI is helping them. We see this with Amarra, a formal gown distributor, which uses AI for inventory management and product descriptions, openly sharing its impact. AI cut content creation time by 60%, reduced overstocking by 40%, and helped keep popular items in stock (Business Insider, 2025). This means AI helped prevent waste and deliver more of what customers want. 

• Handle issues quickly. The AI safety startup Preamble did this when their researchers found vulnerabilities in OpenAI’s GPT-3 model, specifically related to prompt injection attacks that could manipulate the model’s outputs. (Arstechnica, 2022) This both helped focus OpenAI’s efforts on making future models less susceptible, and earned Preamble recognition in the AI research community.

Deploying AI without robust testing can harm brand trust. But the reverse is also true – showcasing ethical AI use can bolster trust. 

If your brand uses AI in a way that clearly benefits customers (like faster customer support responses) and you’re transparent and responsible, it might improve your brand image as forward-thinking and customer-centric.

In essence, people trust content that feels authentic, relevant, and honest – whether AI is involved or not. 

If AI helps you hit those marks, great. If it detracts (makes content feel canned, lazy, or untrustworthy, or doesn’t help with conversions), then you need to adjust course. 

Trust is hard to earn and easy to lose, so any AI strategy in marketing should be filtered through the question: “Will this help maintain or build trust with our audience?” If the answer is not a confident yes, rethink the implementation.  

Brands that thoughtfully integrate AI (invisible hand when you don’t need to see it, and proudly displayed when it adds clear value and requires disclosure) are likely to fare best in the court of public opinion.

Is The Use Of AI Ethical?

Not without guidance – which creates a big opportunity for human copywriters who can guide AI ethically.

AI is reshaping copywriting and marketing, just like the internet did. There’s tremendous opportunities to improve efficiency, personalization, and insights. The future of AI copywriting and marketing is human management.

“With great power comes great responsibility.” – Uncle Ben, Spider-Man (Marvel Comics, Amazing Fantasy #15, 1962)

We’ve discussed how unaddressed issues like bias, privacy violations, or misinformation can quickly turn an AI win into a marketing disaster. 

• Real-world case studies – from Amazon’s biased hiring algorithm to Burger King’s voice-assistant prank gone wrong – show how AI without ethical oversight can harm users and brands alike. 
• On the flip side, we’ve seen that applying ethical principles (fairness, transparency, accountability, etc.) and following best practices lets marketers tap AI’s benefits while safeguarding their audience and reputation. 
• Legal frameworks such as GDPR and FTC guidelines are increasingly requiring this due diligence, essentially codifying ethical use into law. 
• Through expert insights and industry stats, one theme became clear: success in modern marketing isn’t just about using AI efficiently– it’s about using AI ethically. That means keeping humans in the loop, being honest with your audience, respecting privacy, and continuously monitoring AI’s performance and impact.

At the end of the day, ethical AI use in marketing isn’t a hindrance – it’s an enabler of long-term value. 

Using our AI tools more responsibly lets us create content that is more inclusive, accurate, and trustworthy. Brands that champion transparency and fairness will likely find that consumers reward them with loyalty. 

We can’t treat it as a gimmick or shortcut. It’s a powerful tool to be guided by human values. 

In a world increasingly flooded with automated content, quality and integrity stand out. Ethical AI helps us make more valuable, transparent, and trustworthy content for everyone. Customers feel respected and safe, marketers achieve their goals without crossing lines, and society at large benefits from tech helping great products get into the hands of people that want them.

As an AI copywriter, you can start implementing what we’ve covered right away. 

• Audit your AI tools and content – Compare your AI outputs against the checklist.
• Address gaps and risks – Create an action plan, whether it’s adding a disclosure in your contract that you use AI or retraining a model with better data.
• Stay updated on regulations and industry standards – Know what laws apply to the writing you do to stay compliant.
• Adopt a mindset of continuous improvement –
  • Check your analytics, if you’re publishing AI-supported copy and content
  • Adjust or remove AI-supported processes that aren’t contributing to better marketing.
  • Customize AI tools to better align with your brand voice.
• Build trust through active AI management – Share your behind-the-scenes with your audience. Get customer consent around data you’d like to collect.

When you actively manage AI and its ethical implications, you’ll position your brand as a leader in this new era of AI marketing. AI is reshaping marketing, but its foundation – trust – hasn’t changed.

Who Can Help Me Use AI Ethically In My Marketing And Copywriting?

I write and optimize websites that prioritize truth, accuracy, and transparency — and still rank in AI suggestions. Let’s chat.