Has artificial intelligence already taken your job?
Probably not, but the headlines don’t lie: AI technology has reshaped content creation, from auto-generating blog posts to optimizing personalized marketing campaigns.
Consumers now expect hyper-relevant experiences, making AI-driven personalization less of a competitive edge and more of a necessity to serve baseline expectations. That means, even if you’re an AI skeptic — and there are reasons why you could be one — you need to stay informed to avoid falling behind, or to endanger your business because of AI’s data bias.
You see, an AI application doesn’t just automate efficiency — it also scales human bias. Since machine learning models feed on existing data (which often reflects societal biases), we as marketers must be mindful of the risks. Left unchecked, biased AI-generated content can alienate audiences and damage brand trust.
Here’s how you keep your brand messaging on track and ethical.
What’s AI Bias, and Where Does Fairness Come Into Play for Content Creation?
We tend to value human-created content for its creativity and nuance. But when we’re dealing with an AI system, we tend to forget algorithmic bias, because data science lives in this strange bubble where the faceless machine implies authority.
And yet, even the system with the best model performance will inevitably suffer from systemic bias. There’s just no way around it. An AI algorithm might seem like a neutral tool, but because it learns from human-generated data and analyzes it with human-created rules, it inevitably inherits harmful bias — even in ways that aren’t immediately obvious.
Biased outcomes don’t just stem from data — they’re also a reflection of who builds the technology, or embeds a bias mitigation strategy into the system.
Let’s consider what the world of AI development looks like these days to understand the problem. At leading AI conferences, only 18% of researchers are women, and in major tech companies, just 4.5% of employees are Black. These imbalances influence how AI models are trained and whose perspectives are prioritized.
The consequences? Unfair resource allocation, subpar service, civil liberty infringements and even damage to brand reputation.
Just as one example, a 2019 study of Facebook’s advertising algorithm found that ad delivery could skew results in ways advertisers didn’t intend or even realize.
Despite the theoretical opportunities of AI-driven personalized marketing, bias can mean content that reinforces stereotypes or excludes entire demographics — problems that companies can’t afford to ignore.
Can You Create Non-Biased Content With AI Models, Though?
The short answer? Yes — but it takes effort. AI bias isn’t an unsolvable problem, but it also isn’t something that disappears just because a company claims to have “responsible AI.”
If you’re using AI for content creation, the key is intentionality — how you adopt, monitor and refine your AI-driven workflows.
Start by adopting AI gradually instead of rushing to implement it everywhere. Choose platforms that actively work to mitigate bias and have transparent policies. Before even generating content, define your brand values, tone of voice and key messaging — this acts as a filter, ensuring AI aligns with your brand instead of introducing unwanted biases. Then, review and edit all AI-generated content rather than assuming it’s good to go.
You don’t want to turn your entire marketing strategy inside out only to realize that every single email campaign is littered with factual errors.
For companies training proprietary AI models, the challenge is even greater. A diverse dataset is essential — otherwise, you’re just automating existing biases at scale. But even with strong data, bias mitigation is an ongoing process. Regularly analyze AI-generated content, gather feedback and adjust inputs over time to make sure your content stays fair and inclusive.
One way to do this is by using fairness metrics — quantifiable methods for detecting and reducing bias in AI-generated content. Here are a few examples:
- Demographic parity: Ensures AI-generated content represents different groups equally. (e.g., If an AI creates example personas for an ad campaign, does it equally feature different genders, races or age groups?)
- Equalized odds: Measures whether AI predictions or outputs perform equally well across different groups. (e.g., Does an AI-driven hiring tool suggest equally qualified candidates across demographic lines?)
- Sentiment analysis: Uses NLP tools to check if AI-generated content applies disproportionately positive or negative language to certain groups.
- Counterfactual fairness: Tests whether changing a demographic variable (e.g., swapping a male name for a female one) alters the AI’s output significantly.
Using fairness metrics in content audits helps ensure AI-generated messaging doesn’t unintentionally exclude or misrepresent any audience segment.
If the Algorithm Doesn’t Tell You, How Can You Tell If Your Content Is Biased?
AI models won’t flag their own biases for you — beyond the occasional disclaimer, which most users scroll past without a second thought. (Let’s be honest, most of us want to get cracking instead of studying a how-to on using generative AI.)
So, how do you spot bias before it becomes a problem? By actively looking for it.
Start by scanning for patterns.
- Does the AI consistently generate examples featuring the same demographic?
- Are certain perspectives missing?
- Would the argument change if the model used more current data from search?
If the content leans too heavily in one direction, you may need to adjust inputs or diversify your training data.
Next, get human feedback. A diverse review team can catch issues that AI (and even you) might overlook. Yes, we’re all biased, only in different ways. It’s somewhat sad, but also what makes each one of us interesting. That’s why I personally like to get feedback on sensitive subjects from team members, which is very easy, considering Brafton’s international workforce.
You don’t need to switch companies to achieve the same results. But if possible, conduct surveys or audience polls to see if your messaging resonates equally across different groups.
Finally, use bias detection tools. Yes, we’re using AI to fight AI bias, but hear me out. These applications can analyze text, flag stereotypes and highlight areas that might need more balance. AI may not always self-report its flaws, but with the right checks in place, even through a second tool, you can spot and correct bias before it impacts your audience.
Remember That Mitigating Bias Is About People, Not Data
At its core, AI bias isn’t a data problem — it’s a people problem. Algorithms don’t have opinions, but they do reflect the perspectives, priorities and blind spots of the humans who build and use them. That means the responsibility for fair, unbiased content ultimately falls on you — the marketer, editor or business leader leveraging AI.
Bias isn’t always obvious, and we all have our own unconscious perspectives. The difference is that AI scales those biases exponentially, making it critical to proactively build safeguards into your content creation process. Building these processes might require some proactive planning, or even new infrastructure, if the right tools don’t exist for your industry.
Regular audits, diverse review teams and ongoing refinements are all necessary steps to ensure AI-generated content remains ethical, inclusive and aligned with your brand values.
Now, you might feel turned off by the notion of using AI altogether, but remember where we started our little journey. Your competitors are definitely using it, so you have to at least be knowledgeable about it, and there’s no doubt that AI can be a powerful tool. But it works best when paired with human oversight, critical thinking and a commitment to fairness.
The goal isn’t to remove bias entirely (a near-impossible task), but to recognize it, address it and ensure it doesn’t shape your content in ways that exclude or harm your audience.
