Successful AI starts with human behavior.
I apply behavioral science to help organizations understand how people adopt, interact with, and evaluate generative AI.
PhD Researcher, Copenhagen Business School
Why AI success is a behavioral challenge
Organizations are investing heavily in generative AI, yet many still face the same problem: rollout alone does not guarantee value in practice.
Once AI becomes part of real tasks and decisions, value depends not only on what the system can do, but on whether and how people actually use it.
How people adopt and interact with AI is often shaped by psychology: trust, uncertainty, perceived control, and the meanings people attach to the system in use.
My research helps organizations understand these dynamics and design AI systems that people are more likely to trust, adopt, and value.
WHAT THE DATA SHOWS
81%
of people feel uncomfortable relying on a bot to resolve complex queries (Euromonitor, 2024)
49%
of people would wait longer for support from a human to avoid interacting with an AI (NIQ, 2024)
These patterns show that successful AI is not only a technical challenge, but a behavioral one.
A method for studying real human-AI interaction:
Generative Experiments
Generative Experiments allows researchers and organizations to study how people actually interact with AI in controlled but realistic settings. By embedding large language models into experimental environments, the approach makes it possible to observe real human-AI communication, measure trust and decision behavior, and test interaction 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.
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.
Get in touch
Inquiries
Global Presence
Copenhagen Business School / Stanford Institute for Human-Centered AI