As marketers, we love talking about how AI helps us segment smarter and personalize at scale. 

But here’s the catch – if the data (and the people behind it) carry bias, your AI will too. And that can lead to some pretty unfair outcomes, from alienating customers to damaging your brand’s reputation.

In this article, I’ll share real-world examples of where bias shows up and what it looks like in action. More importantly, I’ll show you how to fix it – from auditing your campaigns to diversifying your data and teams – so your marketing isn’t just smart, but also fair and human.

What is AI bias?

Let’s start with the basics – what exactly is AI bias? It’s when systematic data errors lead to unfair outcomes. These biases can arise for a couple of reasons.

First, algorithmic bias happens when your training data is skewed. Maybe it’s using outdated proxies or missing key data points.

Second, there’s human bias. As much as we’d love to think we’re perfectly objective, we all carry biases shaped by our everyday experiences. These inevitably seep into the data we collect and the models we build – which means the outputs can reflect those same unfair patterns.

The real-world impact of AI bias

So what does this actually look like? Here are a few eye-opening examples.

In the US, about 34% of judges are women, yet when you ask an AI model to generate images of judges, only 3% of those images depict women. That’s a massive gap.

Similarly, around 30% of fast-food workers are people of color, but 70% of AI-generated images of fast-food workers show people of color. 

These results create perceptions that are not just inaccurate – they’re outdated and harmful.

The opportunities and risks of AI in marketing

There’s no denying AI offers incredible opportunities – faster segmentation, personalized outreach at scale, and efficiency gains. However, there are also serious threats to consider. 

Mistargeted ads can mean you’re not reaching the right audience. Biases can reinforce stereotypes, like the ones we just saw, and entire communities can get excluded from your campaigns. 

When that happens, you’re not just missing out on diversity – you’re leaving money on the table.

Real-world examples of AI bias

Across industries – from healthcare to finance to beauty – biased data has led to unfair decisions and missed opportunities. The coming examples reveal how those patterns emerge and what happens when we don’t catch them early enough.

Racial bias in healthcare

In 2019, a study found that an algorithm predicted black patients needed less healthcare than white patients. Why? Because the model used money spent as a proxy for health needs.

Since black patients tended to spend less on healthcare – largely due to lack of access – the algorithm incorrectly assumed they needed fewer resources. As a result, less funding was allocated to black communities, deepening existing inequities.

The lesson? If you’re targeting communities based on flawed proxies like spending, ZIP codes, or credit scores, you’re missing a huge part of your potential customer base. Your total addressable market (TAM) could be much larger than what your model suggests.

Gender bias in finance

One surprising example came from Apple’s credit card launch. Despite women having the same or even higher credit scores than men, they were often given lower credit limits.