Battle on bias: AI is learning from our mistakes

Artificial Intelligence (AI): the wonder child of technology that’s revolutionizing everything from how we order pizza to how we design buildings. We’ve got algorithms making us playlists, optimizing our exercise routines, and suggesting the next hot trend in socks. But as this tech powerhouse learns from us, a crucial question crops up: is it learning from our best practices or replicating our worst blunders?

This, dear reader, is the battle on bias—a very human problem that has invaded even the most advanced artificial systems.

If you’re a market researcher, you’re probably on the edge of your seat because you’ve seen this movie before. You’ve faced the beast of bias head-on and wrestled it into submission (most of the time). But here’s the twist—now the beast is powered by AI. So, with that in mind, let’s take a closer look at how AI is learning, why it’s picking up our bad habits, and how market research is uniquely positioned to lead the charge.

Understanding bias: the sneaky culprit in artificial intelligence

Bias. The word itself has a slightly sinister ring to it. But bias is simply the tendency to lean in a particular direction, often unfairly. In market research, bias can mean skewed survey results, inaccurate customer insights, and misguided decisions. Now take that same idea and toss it into the world of AI, and things start to get somewhat problematic.

Just like kids, Artificial Intelligence algorithms models learn by example. We feed them data—tons of data—and they absorb patterns from it. If that data has a skew, if it contains the biases of the people who made it, then congratulations: the AI has now learned to be biased, too. It’s a classic case of ‘garbage in, garbage out.’

Remember Google’s Gemini AI? Its image generator produced some questionable representations—not because the algorithm had a secret agenda, but because it learned from a pool of data tainted by years of human stereotypes and contextual slip-ups.

Bias in AI doesn’t just result in embarrassing outputs, though. It can perpetuate stereotypes, enforce social divisions, and lead to fundamentally unfair decisions. For market researchers, whose bread and butter are data accuracy and consumer insights, AI bias could turn into a nightmare—unless we stay ahead of it.

Bias awareness for market researchers

The truth is, bias isn’t new for market researchers. You folks have been dealing with it for decades, and you’ve developed both a sixth sense and rigorous approach honed through experience for it. Every time a survey respondent gives a questionable answer, or a focus group spirals off-topic because everyone is nodding in agreement—that’s bias showing its hand. Market researchers know better than anyone that what people say they do and what they actually do are often oceans apart. So, you’ve learned to anticipate, adjust, and double-check your findings.

That bias-busting instinct and scientific rigour is what makes market researchers uniquely equipped to grapple with AI bias. If you can understand how bias affects a survey, you can understand how it affects an algorithm. 

So, why does this matter?

Because bias in AI isn’t just inconvenient—it can be outright damaging. When biased data trains an AI model, it can generate outputs that reinforce harmful stereotypes. Take, for instance, image-generating AIs that overrepresent male figures in professional roles while depicting women in domestic settings. Or facial recognition software that struggles to recognize people of certain ethnicities with the same accuracy as others—the consequences of which can be deeply troubling.

Imagine an AI providing a company with skewed market insights—say, overlooking a specific demographic because it doesn’t understand the nuance of their preferences or needs. That’s not just bad business, it’s unethical. It means missed opportunities, poor representation, and potentially alienating whole communities. And, let’s face it—if AI is meant to be our super-intelligent helper, it shouldn’t be enforcing 1950s-era stereotypes.

How market research is ahead of the curve

Market researchers have a unique advantage when it comes to using AI ethically. You’ve already got a well-honed radar for bias mitigation, and you’re used to applying rigorous standards to ensure data quality. You know the importance of diverse sampling, asking the right questions, and avoiding leading questions. These principles are just as applicable when working with AI.

In the world of market research, you wouldn’t dream of putting a biased survey in front of your audience, so why feed a biased dataset to an AI? The key here is realizing that AI isn’t magical; it’s just a reflection of the data you give it. It can’t rise above the quality of the data. But with vigilance and good practice, AI can become an incredibly powerful aid, not just for streamlining research but for enhancing accuracy, avoiding blind spots, and uncovering insights that even the sharpest human eye might miss.

Strategies to avoid bias in AI: tips for market researchers

Now, let’s get practical. If AI is learning from us, how can we teach it to be better? Here’s how some of our market researchers are tackling AI bias head-on:

1. Diverse data: one of the main causes of AI bias is training on a dataset that isn’t representative. The more diverse your data, the better the AI will understand and generalize its findings. Remember, the diversity of your training data should reflect the diversity of your target population.
2. Watch for hidden bias: some biases are easy to spot—like an overrepresentation of a certain group—but others are sneakier. Think about language, context, and even cultural references. Bias can creep in from the way questions are phrased, or from unbalanced datasets that favor one particular group’s experiences. Market researchers are already familiar with rephrasing questions to eliminate bias; now it’s time to rephrase data.
3. Transparency in algorithms: AI models are notoriously black-box-like. If you’re using an AI tool, it’s important to work with providers who can explain what’s going on under the hood. Understand how an algorithm reaches its conclusions, and you’ll be better positioned to evaluate the reliability of those conclusions.
4. Human review: AI can crunch data and spot trends, but it’s the human touch that contextualizes these insights. Market researchers should always serve as the final filter, reviewing AI-generated findings to make sure they’re accurate and free from harmful bias.
5. Expectation management: AI is powerful, but it’s not infallible. Understand what it can do and, more importantly, what it can’t do. An AI can summarize mountains of data, but it might miss the subtlety of human emotion. As market researchers, part of avoiding bias is knowing when to trust your own instincts and experience over an AI’s recommendation.

Developing a framework of how to leverage AI for success can also prove hugely beneficial! If you’re interested in learning how, watch our recent webinar with industry thought leader Mike Stevens, and discover how AI can accelerate your agency’s growth. 

The future: can AI learn from our good side?

Here’s the good news: AI is not doomed to be forever flawed. It has the potential to be our most unbiased teammate yet, but that’s going to require us to be responsible data curators and savvy AI handlers. As market researchers, you already possess a crucial skill set—you understand people, you’re careful with data, and you know how to turn insight into action. When AI learns from the best of human practices, it’s capable of producing insights at an unimaginable scale—insights that are richer, fairer, and, ultimately, more helpful.

It’s on us all to make sure that AI’s education is a good one.

How Forsta can help

With a wealth of experience in avoiding the pitfalls of human biases, we have the power to ensure that AI remains a tool for good—not an amplifier of our worst tendencies. Bias awareness isn’t just an ethical checkbox; it’s the secret sauce that turns AI from a fancy calculator into a revolutionary force for understanding human behavior. Our advanced technology, superior data processing, and flexible reporting capabilities empower you to harness AI effectively, fueling profound human understanding while safeguarding fairness and integrity.

To find out how Forsta’s industry-leading platform can banish bias to reveal more accurate insights, book your demo today.