Human-Gen AI Co-Design: Exploring Factors Impacting Trust Calibration

The process of generating ideas during co-design with a
Generative AI (GenAI) system requires the gradual calibration
of trust in that system. Trust plays a pivotal role in shaping
human interactions with technology, and developing
well-calibrated trust is essential for the effective use and
integration of GenAI. Proper trust calibration helps prevent
underutilization of the system’s capabilities and dissatisfaction
with its output. For engineers and system designers, trust is
particularly important as it directly influences user responses,
system adoption, and overall engagement with new
technologies. To explore the factors that influence trust
fluctuation when co-designing with a GenAI system, we
analyzed 12 hours of conceptual human-AI co-design sessions
using a custom GenAI system capable of producing images
across various generation modes from convergent-divergent to
abstract-concrete, and combining text and sketch prompting.
Focussing on each moment of interaction with
GenAI-generated images, we conducted an incremental and
qualitative coding of each trust-related extract from think-aloud
protocols. Through this approach, we identified 23 key factors
that cause fluctuations in trust. Our findings reveal a complex
network of factors that impact trust calibration, offering
insights into how GenAI systems can be designed to facilitate
faster and more effective trust-building in human-GenAI
collaborations.