In this conversation, behavioral data scientist Ganna Pogrebna discusses how human behavior, bias, and decision-making shape AI systems. She explains that nearly all algorithms are trained on human-labelled data—carrying human biases into machine outputs—using examples like Amazon’s hiring algorithm and video-based interview tools that rated identical answers differently depending on background visuals. Pogrebna emphasizes that truly effective AI-human collaboration begins with understanding these biases, using AI intentionally for creativity rather than precision, and developing strong prompting skills that provide context and purpose. She also warns against relying blindly on AI-generated information, highlighting the importance of verifying sources and maintaining personal accountability in team environments where misinformation can damage human reputation.

The discussion extends into the future of human-AI teams, where Pogrebna argues that organizations should treat AI as a brainstorming partner rather than a perfect advisor. She explores how digital twins and behavioral simulations can support strategic decision-making, from content creation to stakeholder modelling, as long as they are grounded in real human data and supplemented with direct customer engagement. While machines and humans think differently—machines requiring far more examples to learn—Pogrebna believes the real challenge lies not in AI design but in teaching people to recognize, collaborate with, and properly communicate with machines. She concludes with an optimistic outlook: if we embrace AI as a tool for augmentation rather than control, we can uncover meaningful benefits for individuals, teams, and businesses.