Authors: Marie Hense, Rick Candelaria
Trust is the new benchmark for AI
Artificial intelligence (AI) has become one of the most powerful tools in modern research, capable of analyzing vast datasets, identifying patterns, and generating insights at speeds no human or traditional research could match. But speed and scale mean little without trust. AI may be intelligent, but it takes validation to make it reliable.
At Toluna, quality has always been the foundation of credible insights. As AI models take on more responsibility in day-to-day research, our role is to ensure they do so accurately, ethically, and contextually. True quality still requires human expertise: the ability to interpret, challenge, and refine what technology produces.
The next evolution of research will be powered by synthetic data and AI-generated personas: highly complex simulated respondents that can mirror human opinions, emotions, and behaviors at scale. When developed with quality and diversity in mind, synthetic personas allow us to explore markets faster, test scenarios that would otherwise be impractical, and uncover trends before they fully emerge.
We view synthetic personas as an extension of real human insight, not a replacement for it. They will enable us to validate hypotheses, stress-test ideas, and enhance understanding where traditional data collection may be limited. By ensuring that these personas are built on high-quality, authentic, and representative foundations, we can achieve both scalability and depth – a rare combination in research.
Human oversight keeps AI grounded in reality
Quality validation bridges the gap between artificial accuracy and genuine understanding. It means scrutinizing the base data that fuels AI models, considering the markets and cultures they represent, and applying human judgement where algorithms might overlook nuance. Quality validation is integral to providing transparency and trust for clients.
There are several ways to validate AI models effectively. For starters, cross-validation and benchmarking against known human data help confirm that predictions hold up beyond training environments. Expert review panels can test model reasoning for cultural or contextual misinterpretations, while anomaly detection and outlier analysis reveal inconsistencies that signal deeper bias.
Comparing outputs from synthetic personas against real respondent data is another valuable step. This ensures that AI-driven simulations mirror authentic human diversity and behavior. In short, robust validation blends quantitative checks with qualitative assessment, keeping technology anchored in reality – a responsibility we take very seriously at Toluna.
By combining automation with expertise, we create a system that’s both intelligent and trustworthy. One that enhances, rather than replaces, human insight.
Innovation with integrity
As AI becomes more deeply embedded in research, validation isn’t the final step. It’s an ongoing discipline from inception via development, all the way to the application of an AI model. By maintaining the human touch behind our intelligent systems, we ensure that innovation always serves insight, and that technology continues to reflect the diversity and integrity of the people it seeks to understand.
