Successful AI starts with human insight.
Applying behavioral science to help organizations understand how people adopt, interact with, and evaluate generative AI.
Generative Experiments
A signature research approach that embeds large language models into controlled experimental environments. Moving beyond hypothetical responses, this method observes real human-AI interaction, measures behavioral trust, and evaluates communication patterns before AI systems are deployed in practice.
Research Focus
Trust & Behavioral Adoption
Measuring how users calibrate trust and bridge the gap between AI potential and actual behavioral integration.
Human-AI Collaboration
Studying real-time dynamics between human expertise and generative models during complex decision-making processes.
Evaluation & Communication
Evaluating the quality of AI-supported outputs and the nuance of communication between humans and large language models.
Why the human element is the missing variable
Truly effective AI implementation begins with understanding human response. Behind every successful deployment is a foundation of trust and behavioral alignment. Measuring these variables in real-world scenarios allows organizations to move beyond hypothetical models and design for responsible adoption. By observing how people actually collaborate with generative outputs, we can ensure that AI becomes a trusted partner rather than a source of professional friction.
Bridging Theory and Practice: Speaking & Collaboration
Siv engages with organizations through speaking, workshops, and advisory work, focusing on how people respond to and collaborate with generative AI. Her expertise helps teams design systems tethered to real human behavior rather than hypothetical assumptions. She is available for collaborations that aim to understand and improve human-centered AI interaction in real-world settings.
Siv Tinangon Pedersen
Siv Tinangon Pedersen is a PhD researcher at Copenhagen Business School studying how people interact with generative AI in real decision situations. Her research focuses on trust, adoption, and the evaluation of AI-supported outputs, bridging the gap between theoretical potential and human behavior.
Currently a visiting researcher at Stanford University’s Institute for Human-Centered Artificial Intelligence (HAI), Siv brings a background in data-driven strategy and analytics from her prior work at PwC and Celonis. She holds a unique perspective that combines rigorous academic methodology with practical organizational experience.
Her signature approach, Generative Experiments, embeds large language models into controlled environments to observe real collaboration. Siv’s work helps organizations worldwide understand when people trust AI and how to design systems that truly serve human needs.
Insights
Deep dives into behavioral science, trust, and the real-world application of human-centered AI evaluation.
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Inquiries
Global Presence
Copenhagen Business School / Stanford Institute for Human-Centered AI