6 Ways to Use Synthetic Research Responsibly

Synthetic research — any research that is based on artificially-generated data made to mimic real-world data — is a powerful tool to have in your toolkit. At One North, synthetic research has made it possible for our legal clients to gain insights about buyers they otherwise wouldn’t have access to. 

While the possibilities of synthetic research are endless, it can just as easily mislead as enlighten. Synthetic data mimics the biases found in the data it was trained on. When teams don’t address this, it results in real-world harm, like resume screeners penalizing the word ‘women.’ 

It’s best practice in any research project to minimize the impact of bias. Our team is constantly examining how bias shows up in generative AI, and we’ve learned what truly helps and what doesn’t. With that experience in mind, here are several practical ways you can minimize bias and maximize impact when incorporating synthetic research into your work.

1. Determine Why Synthetic Data Is Necessary 

Synthetic data is not a one-size-fits-all solution and should only be used when there are concrete reasons real-world data isn’t accessible. Common reasons data might not be available include: 

• Privacy concerns
• Limited access
• Budgets and timelines

If you’re dealing with sensitive information or a hard-to-reach population, for example medical information about people with a rare disease, that’s a great reason to use synthetic data. Our team has conducted surveys with synthetic personas to gather insights from leaders in niche audiences, allowing us to gather data in a few weeks instead of months, or not at all.

2. Define Fairness for the Dataset  

We all want to minimize bias in our data and ensure we’re being fair in how we use it. However, what fairness means depends on your context. To get ahead of this, explicitly plan for how you’ll address biases that likely exist in your data. In market research, this could mean avoiding demographic skew. It might also mean oversampling from an underrepresented group, like people using assistive technology.

3. Supervise Building the Dataset 

People with domain expertise should be involved with building the dataset that will inform your synthetic data. Collaborate across teams to ensure the dataset reflects real-world complexity and avoids reinforcing existing biases. Make sure the team is using data that is relevant to your context and goal. For example, customer service logs that are 85 percent password reset requests likely won’t inform how customers might react to a new feature.

4. Test for Stability

A stable dataset performs consistently across scenarios. Gather your synthetic data using multiple prompts to ensure you receive similar data results even when the prompt is adjusted slightly. Run stress tests and simulations to confirm your synthetic data holds up under pressure.

5. Ensure Similarity to Existing Data 

Involve people familiar with your context to ensure your synthetic data seems plausible. Synthetic data should echo the patterns of real data without duplicating it. Consider what you already know about your population and confirm it appears in the data you gather. If you’re using generative AI to run a concept test, and you know that your user base needs large text for legibility on the go, make sure that the output of your test supports that.

6. Validate High-Risk Decisions With Real People 

Synthetic data can guide strategy, but it shouldn’t replace human judgment. For decisions with ethical, financial, or reputational risk, bring in real users, stakeholders, or experts to validate findings. 

Synthetic data is a powerful tool, but when used irresponsibly, it can lead you astray. The key is approaching it with intention — understanding when it’s the right solution, how to build it responsibly, and where human validation remains essential. When done right, synthetic research doesn’t replace traditional methods; it expands what’s possible while keeping fairness and accuracy at the center.

Want to explore how synthetic research could unlock insights for your organization? Let’s discuss how One North can help you leverage AI-powered research while maintaining the guardrails that matter. 

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