Understanding, Mitigating, and Leveraging Cognitive Biases to Calibrate Trust in Evolving AI Systems
Saumya Pareek (University of Melbourne)
Nattapat Boonprakong (National University of Singapore)
Naja Kathrine Kollerup (Aalborg University)
Si Chen (University of Notre Dame)
Simo Hosio (University of Oulu)
Koji Yatani (University of Tokyo)
Yi Chieh Lee (National University of Singapore)
Ujwal Gadiraju (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Niels van Berkel (Aalborg University)
Jorge Goncalves (University of Melbourne)
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Abstract
Despite decades of advancements in Artificial Intelligence (AI), fostering appropriate trust in AI systems remains a challenge. Cognitive biases - systematic deviations from rational judgement - profoundly influence human decision-making, and reliance on such “mental shortcuts” can make AI systems appear more or less trustworthy than they really are, often undermining collaboration outcomes. As AI evolves with more sophisticated and persuasive natural language outputs, particularly through Generative AI (GenAI) and Large Language Models (LLMs), these biases may manifest in new and unpredictable ways, calling for their comprehensive examination. This workshop brings together diverse researchers from HCI, human-centred AI, cognitive psychology, interaction design, and related fields to collaboratively explore how cognitive biases influence trust calibration in human-AI interaction and establish a research agenda. We will explore how biases emerge across the human-AI interaction pipeline, what design strategies can mitigate or even harness these heuristics, and what methods are needed to study these dynamics effectively. Through a highly interactive 90-minute session, participants will map out open challenges, brainstorm tensions and solutions, chart future research directions, and share perspectives from their own diverse disciplinary lenses. Through this workshop, we aim to build a shared understanding of how cognitive biases influence trust in evolving AI systems, and derive a forward-looking, bias-aware research agenda that promotes appropriate trust in human-AI interaction.
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