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  • Writer's pictureLiz Tassey

Avoiding True Lies: Maintaining Credibility While Using AI in Research

Updated: Feb 8


True Lies Movie Poster with Arnold Schwarzenegger

Recently the Beehive AI team chatted with a researcher at a leading tech organization. He recounted the story of his child who used ChatGPT to inform a school assignment on a historical figure. At first glance, the output looked pretty solid. But when he asked his child to go verify the facts presented, about 20% of the facts ended up being made up. He told us “If you create a report with insights that are not true, that is the worst thing that can happen as a researcher.” 


These “true lies” - data hallucinations, biases, verification issues, and other credibility threats - are unfortunately not uncommon, and present a fundamental challenge to the promise of AI innovation. 




For researchers, this is a high stakes situation.  Their reputations and careers are wholly based on the credibility of their insights and the veracity of the data they use in analysis. While AI holds promise to accelerate key elements of research, unlock new analysis capabilities, and improve the ability to analyze data at scale, solutions must be evaluated and implemented with a thoughtful data strategy and a skilled approach to building the supporting models.




Avoiding Data Hallucinations and other AI pitfalls


The good news is that organizations can prevent these issues and confidently incorporate AI into their research programs. And no, you don’t need Arnold Schwarzenegger (though the Terminator showing up at your next insights presentation would be cool). But you do need to have a strategy for evaluating and implementing AI. Here are a few tips to get your started: 


  1. Evaluate purpose-built AI: Generic AI solutions are more prone to data hallucinations, as the data they are trained on is also generic, and it cannot learn effectively from varied, unrelated task completion. Be clear about the problem you are trying to solve, and the expected outcomes, and consider solutions designed for your specific purpose.

  2. Use high-quality training data: AI models rely on input data to complete tasks and deliver desired outcomes. The quality, relevance, and diversity of that data will significantly impact the effectiveness of the AI solution, helping you avoid data hallucinations and biases.  Look for solutions that can ingest your specific business data and build tailored models that can understand your unique business and customer context.

  3. Ask AI to prove it: A top concern is the black box nature of some AI solutions – getting an answer or insight without being able to explore the data behind it. AI solutions should be able to surface relevant, contextual data (e.g. a representative quote) that is being leveraged to inform the result.

  4. Maintain ongoing human oversight and validation: The beauty of generative AI is that it can automatically learn and adapt.  However there should always be human oversight to ensure the models are staying on track. As you evaluate AI solutions, be sure they include a mechanism for providing feedback on outputs and task completion. 


AI has huge potential to unlock insights and help organizations make faster, more confident decisions. As long as researchers (and their partners across IT and Data teams) pursue AI solutions with the right strategic considerations, they are poised to take their research to the next level, while maintaining their credibility and reputations.



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Learn more how Beehive AI can help you deliver faster, credible insights from your research


Beehive AI provides a generative AI platform designed specifically for an organization’s unique qualitative data. With self-learning language models, validated by human experts, and built-in statistical analysis, research and insights leaders can quickly, accurately, and safely analyze their qualitative data, and combine it with quantitative data, to generate more robust customer insights. 


Interested in learning more? Schedule a demo today.




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